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Article

Motivational Factors Influencing Ethiopian Student Teachers’ Self-Efficacy in Adopting AI in Education

by
Adula Bekele Hunde
1,*,
Eyvind Elstad
1,
Knut-Andreas Abben Christophersen
2,
Are Turmo
3,
Fekede Tuli Gemeda
4 and
Eyueil Abate Demissie
4
1
Department of Teacher Education and School Research, University of Oslo, 0317 Oslo, Norway
2
Department of Political Sciences, University of Oslo, 0317 Oslo, Norway
3
The Norwegian Centre for Science Education, University of Oslo, 0317 Oslo, Norway
4
Department of Curriculum and Instructional Sciences, Kotebe University of Education, Addis Ababa P.O. Box 31245, Ethiopia
*
Author to whom correspondence should be addressed.
Educ. Sci. 2026, 16(5), 800; https://doi.org/10.3390/educsci16050800 (registering DOI)
Submission received: 6 March 2026 / Revised: 9 May 2026 / Accepted: 15 May 2026 / Published: 19 May 2026
(This article belongs to the Special Issue Holistic Education: What It Is and How It Works)
Browse Figures
Versions Notes

Abstract

Understanding the motivational factors influencing student teachers’ self-efficacy in adopting Artificial Intelligence (AI) is essential in technology-driven learning environments, but this area has received less research attention in resource-scarce settings like Ethiopia. To this end, this study was initiated to explore the motivational factors influencing the self-efficacy in adopting AI among Ethiopian student teachers. The study employed structural equation modeling to analyze data collected from 278 student teachers enrolled in teacher education programs to determine the relationship between motivational factors (commitment to the teaching profession, along with intrinsic, extrinsic, and altruistic motivations) and dimensions of self-efficacy (teaching AI skills, planning and classroom management, and student affective domains). The result demonstrated that strong and positive associations were found between affective commitment to teaching and self-efficacy (p < 0.01) in AI teaching skills, planning and managing the classroom, and addressing the student affective domain. In addition, positive and moderate associations were noted between extrinsic motivation and self-efficacy (p < 0.05) in the student affective and teaching AI skills domains. No significant relationships were observed for intrinsic or altruistic motivations. Thus, by highlighting the role of commitment and extrinsic motivation, the findings can inform teacher education programs aiming to enhance the holistic development and effectiveness of future educators and contribute to developing targeted recruitment and training strategies that nurture motivated and technologically proficient teachers.

1. Introduction

Holistic education, an approach that seeks balanced intellectual, social, emotional, and physical growth rather than a narrow focus on academic attainment, is highly needed in contemporary schools (Miseliunaite et al., 2022; UNESCO, 2015). Realizing this vision requires preparing teachers who are themselves holistically developed and able to foster such development in their students once deployed to schools (Darling-Hammond et al., 2025). Artificial Intelligence (AI) is increasingly becoming a powerful tool for supporting holistic education and development, particularly when it is systematically integrated and its use is pedagogically informed (Bower et al., 2024; Brandão et al., 2024). Beyond enabling holistic education, AI-related competence is itself emerging as one of the most essential skills that citizens need to navigate and succeed in an ever-changing world of work (Carolus et al., 2023).
The increasing demand for AI skills is reflected by companies attracting large numbers of employees and reaching out to a significant proportion of society through their products and services. For instance, a survey conducted by IBM indicates that around the end of 2023, 45% of large companies in developed countries incorporated AI into their services, while 40% of companies were actively exploring its potential (IBM, 2023). The incorporation of AI into business functions has made AI skills some of the most sought-after abilities for individuals aspiring to lead fulfilling lives and for companies aiming to thrive in a competitive, customer-oriented marketplace. In other words, understanding AI is crucial not only for experts but also for the average person, as awareness of what AI is, what it can accomplish, and how it can be beneficial is becoming increasingly important. Individuals with AI skills are likely to excel in environments rich in AI technology, while those with low AI literacy may struggle when required to interact with AI (Margaryan, 2023). Individuals with AI skills also get instant and personalized support from the AI system that can help them reduce stress, increase accessibility to resources and help manage workload, which are key for professional and personal wellness (C. Chan, 2024). As a result, there is an urgent need for educational institutions to address this demand by equipping the younger generation with the necessary understanding and skills to utilize AI tools and functionalities effectively.
Despite the growing body of literature on AI integration in educational contexts, a substantial knowledge gap persists regarding the specific motivational factors influencing student teachers’ self-efficacy in adopting AI in Ethiopia and other regions. Recent systematic reviews on AI usage indicate that existing studies primarily focus on developed countries (Li et al., 2024), where language education (Deng et al., 2025) and computer science education (Ng et al., 2023) receive more attention than the overall experiences of student teachers. Similarly, research from Ethiopia often centers on broader educational technologies without thoroughly examining the specific cultural and contextual factors affecting Ethiopian student teachers (Michael, 2025; Woldemariam et al., 2025). Moreover, based on the authors’ review of recent publications from studies conducted in Ethiopia and published in national and international journals, no articles have yet examined the relationship between student teachers’ motivation for teaching and their self-efficacy in adopting AI in their teaching. Instead, the available studies call for a systematic investigation into the effective utilization of AI in teacher education (Opoku et al., 2025; Tuli et al., 2025). This study aims to address this gap by investigating the impact of motivational factors—such as long-term commitment to the teaching profession, intrinsic, extrinsic, and altruistic motivation—on shaping student teachers’ self-efficacy in adapting AI in their teaching practices.

1.1. The Context

The establishment of teacher education in Ethiopia coincided with the end of World War II. The period from 1945 to 1974 is often regarded as the golden age for the teaching profession and teacher education in Ethiopia (Semela, 2014). During this time, the teaching profession was highly sought after, and teacher education programs recruited exceptional candidates from among secondary school graduates. However, the status of the teaching profession gradually declined, leading to a decrease in the number of academically successful secondary school graduates choosing teacher education programs as their first preference (Mekonnen, 2008). This decline has adversely impacted the quality of teachers and students learning across various levels of education.
Ethiopia has been working for years to enhance the quality of teacher education (Woldemariam et al., 2025), and particularly at this time, the government is investing to facilitate the provision of technology-based education (Ministry of Education, 2021). Various reforms and strategies have been developed to improve the overall quality of education, particularly by strengthening the capabilities of teacher education institutions at both the regional and national levels. A series of initiatives has been implemented to restore the teaching profession to its former prominence. For instance, following the recommendations of the 15-year education roadmap (Ministry of Education, 2021), Kotebe University of Education—which is the focus of this study—was re-established by parliament as the sole national university dedicated to excelling in teacher preparation and research (Tuli et al., 2025). Since then, Kotebe University of Education has gradually phased out all non-teaching programs, focusing exclusively on teaching-related programs, research, and service activities. Additionally, the Ministry of Education has identified five universities with Colleges of Education, nurturing them to excel in training teachers and school leaders. Currently, these five universities and Kotebe University of Education are the primary institutions preparing teachers for secondary schools. The Ministry of Education has been supporting Kotebe University of Education in particular, facilitating the placement of top-performing secondary school leavers, while other university-based centers of excellence are admitting students who choose teaching as a career.

1.2. Theoretical Framework

The theoretical foundation of this research is based on Bandura’s concept of self-efficacy, which refers to teachers’ belief in their ability to effectively facilitate student learning and adapt instruction as needed. This belief, in turn, is reflected in their actual teaching practices (Bandura, 1977). Teaching self-efficacy encompasses various dimensions, several of which have been identified in the literature on classroom teaching.
One critical dimension, referred to by various terms, is the student affective domain, which reflects teachers’ belief in their capacity to motivate and engage students in the learning process (Burić et al., 2024; Hußner et al., 2024). Another dimension is subject or tool-specific efficacy, which pertains to teachers’ belief in their ability to teach a specific subject or utilize a particular tool effectively (Garvis & Pendergast, 2011; Menon et al., 2024; Perera & John, 2020; Yang et al., 2024). Additionally, Friedman and Kass (2002) highlight planning and classroom management as another critical dimension of teaching efficacy, indicating teachers’ confidence in creating a conducive learning environment and managing student behavior. Furthermore, Hußner et al. (2024) and Na and Isa (2024) identify instructional strategies as a significant aspect of teaching efficacy, emphasizing teachers’ belief in their ability to apply teaching methods and adjust instruction as needed. However, we did not include the latter-mentioned dimension in the study, as our focus was not on the teachers’ efficacy in applying a specific teaching method.
Drawing on this evidence, we have identified that student teachers’ self-efficacy in adopting AI in their teaching practices encompasses three critical dimensions that significantly impact their development as educators. These dimensions are self-efficacy in teaching AI skills, self-efficacy in adopting AI in planning and classroom management, and self-efficacy in the student affective domain (see Figure 1). Each of these dimensions highlights the emotional and cognitive aspects of teaching and learning, and their interconnectedness adds value in fostering a positive educational environment.
The present study’s primary aim is to explore the motivational antecedents influencing self-efficacy in adoption of AI among Ethiopian student teachers. The specific objectives are examining the relationship between long-term commitment to the teaching profession and self-efficacy in using AI; analyzing the role of altruistic, intrinsic, and extrinsic motivations for teaching in shaping self-efficacy; and identifying potential implications for teacher education programs seeking to enhance the effectiveness of future educators in technology-rich educational environments.

1.3. Conceptual Framework and Hypothesis Development

This study focuses on the critical role of motivational factors among Ethiopian student teachers in determining their self-efficacy in adopting AI in their teaching practices. We primarily rely on existing motivational theories, including the Self-Determination Theory of Motivation (Ryan & Deci, 2000), Social Cognitive Career Theory (Lent et al., 1994), and Schwartz’s Theory of Human Values (Schwartz, 2012) to explore and understand how these motivational factors shape teachers’ self-efficacy.
The Self-Determination Theory of Motivation originates from the fundamental concept of self-determination, which refers to an individual’s capacity to manage oneself, make confident choices, and think independently (Deci, 1971). This theory posits that internal sources of motivation (intrinsic motivation)—such as learning to gain independence and demonstrating excellence—are primary drivers of motivation (Ryan & Deci, 2000). Furthermore, Ryan and Deci emphasize the role of external factors (extrinsic motivation) like rewards, money, and appreciation as significant motivators for student learning. Consequently, both extrinsic and intrinsic motivation enhance self-efficacy and foster innovative behavior (Ahmadi et al., 2023).
Social Cognitive Career Theory highlights the desire to help others (altruistic motives) as another crucial motivational factor influencing individuals’ self-efficacy (Lent et al., 1994). According to this theory, personal beliefs, expectations, and goals significantly influence personal decision-making. Research on the determinants of teachers’ self-efficacy indicates that teachers with altruistic motives (the desire to help others) tend to exhibit high self-efficacy and job satisfaction (Chang & Sung, 2024). Thus, student teachers with a strong desire to benefit their learners (altruistic motives) contribute more to educational improvement (Roness & Smith, 2009) by establishing a robust foundation for self-efficacy development in three domains: teaching AI skills, classroom management, and addressing student needs (Roness, 2011).
Schwartz’s Theory of Human Values serves as the third theoretical framework for understanding the motivational factors influencing student teachers’ self-efficacy in using AI in their teaching. This theory posits that when individuals reflect on their personal values, they consider aspects important to their lives, such as security, independence, success, kindness, and pleasure. Thus, possessing or anticipating high personal values is associated with enhanced self-efficacy (Schwartz, 2012). In this regard, long-term commitment to teaching reflects a strong sense of personal value, suggesting that student teachers with such beliefs and expectations would demonstrate high self-efficacy in using AI in their teaching (Barni et al., 2019). Moreover, recent studies confirm that affective commitment to the teaching profession acts as a critical mediating factor that amplifies the positive impacts of all motivational types on self-efficacy development (Octoria et al., 2024).
These three motivational theories provide the dimensions of motivation (intrinsic motives, extrinsic motives, altruistic motives, and long-term commitment to teaching) that serve as the framework for our study, as shown in Figure 1.
Intrinsic motivation vs. self-efficacy in adopting AI in teaching: Studies show that student teachers’ intrinsic motivation for teaching influences their self-efficacy in adopting AI. A recent large-scale empirical study based on 748 student teachers from 12 universities identifies intrinsic motivation as a leading factor determining student teachers’ adoption of AI in their teaching (Şimşek et al., 2025). Hazzan-Bishara et al. (2025) confirms that intrinsic motivation is a critical driver encouraging schoolteachers to explore and adopt AI tools in their teaching. Yang et al. (2024) adds that teachers who experience high personal satisfaction from enhancing student learning outcomes are more likely to explore innovative teaching tools such as AI. Intrinsic motivation, in general, enhances teachers’ sense of efficacy to utilize AI tools (Tabernero & Hernández, 2011). Moreover, in the study by Martínez-Moreno and Petko (2024), student teachers who positively evaluated the value of AI when deciding to join a teacher education program are more likely to use AI tools, even though AI was not a significant factor in determining the choice of teaching as a career for the majority of aspiring teachers. Therefore, intrinsically motivated student teachers are more likely to view AI as a valuable tool to enhance their teaching, rather than a threat, leading to higher self-efficacy in using AI tools for planning of instruction and facilitating student engagement (S. Chan et al., 2023). Building on this foundation, the following hypotheses are proposed:
H1. 
Student teachers’ intrinsic motivation towards the teaching job significantly predicts their self-efficacy in teaching AI skills.
H2. 
Student teachers’ intrinsic motivation towards the teaching job significantly predicts their self-efficacy in using AI in planning and classroom management.
H3. 
Student teachers’ intrinsic motivation towards the teaching job significantly predicts their self-efficacy in addressing the student affective domain.
Extrinsic motivation vs. self-efficacy in adopting AI in teaching: Decisions of student teachers with extrinsic motivation for teaching are influenced mainly by what teaching jobs could offer them, including job opportunity, career advancement and workload reduction (Bergmark et al., 2018; Calkins et al., 2024). Similarly, student teachers’ perception of AI as a tool for attaining these goals leads towards early development of self-efficacy in adopting technologies (Martínez-Moreno & Petko, 2024). Thus, extrinsic motivations encourage high self-efficacy of using technology provided that it leads to perceived personalized benefits, such as better classroom management, improved instruction, and personalized student outcomes (Ding et al., 2025; Zhao et al., 2025). On the contrary, if teachers perceive AI as a burden and a threat to their job, they tend to resist the practice and remain in doubt about their ability to adopt AI in teaching (Zhao et al., 2025). With these considerations, the following hypotheses are proposed for further investigation:
H4. 
Student teachers’ extrinsic motivation towards the teaching job significantly predicts their self-efficacy in teaching AI skills.
H5. 
Student teachers’ extrinsic motivation towards the teaching job significantly predicts their self-efficacy in adopting AI in planning and classroom management.
H6. 
Student teachers’ extrinsic motivation towards the teaching job significantly predicts their self-efficacy in addressing the student affective domain.
Altruistic motivation vs. self-efficacy in adopting AI in teaching: Some student teachers enter teacher education with a desire to contribute to the development and well-being of students and younger generations through their work as teachers (Bergmark et al., 2018). Accordingly, when student teachers perceive AI as a valuable tool for enhancing student learning, they are more likely to explore, understand and develop a feeling that they can integrate it in their teaching practice (Hazzan-Bishara et al., 2025; Martínez-Moreno & Petko, 2024). Based on this assumption, the following hypotheses are proposed for investigation:
H7. 
Student teachers’ altruistic motivation towards the teaching job significantly predicts their self-efficacy in teaching AI skills.
H8. 
Student teachers’ altruistic motivation towards the teaching job significantly predicts their self-efficacy in adopting AI in planning and classroom management.
H9. 
Student teachers’ altruistic motivation towards the teaching job significantly predicts their self-efficacy in addressing the student affective domain.
Commitment to teaching profession vs. self-efficacy in adopting AI in teaching: A long-term commitment to the teaching profession motivates student teachers to view AI as an important tool for their future success and drives them to invest time and energy in exploring AI tools and mastering their use and application in teaching (Ding et al., 2025). These student teachers are more likely to seek out AI-related training, participate in professional development, and engage in hands-on AI activities, which in turn build their mastery of AI and strengthen their self-efficacy in applying it in teaching (Chiu et al., 2026). Based on these premises, the following hypotheses are proposed for further exploration:
H10. 
Student teachers’ commitment to the teaching profession significantly predicts their self-efficacy in teaching AI skills.
H11. 
Student teachers’ commitment to the teaching profession significantly predicts their self-efficacy in adopting AI in planning and classroom management.
H12. 
Student teachers’ commitment to the teaching profession significantly predicts their self-efficacy in addressing the student affective domain.
Accordingly, we consider these four motivational dimensions as independent variables in our research. We believe that these motivational factors can influence three dimensions of self-efficacy: self-efficacy in teaching AI skills, self-efficacy in planning and managing the classroom, and self-efficacy in the student affective domain, which are considered dependent variables. For instance, S. Chan et al. (2023) indicate that intrinsic motivation may underpin the growth of self-efficacy across these three domains. In other words, we hypothesize that each type of motivation factor contributes uniquely to self-efficacy development across the three dimensions (see Figure 2).
In sum, though we are interested in testing the influence of each motivation factor across the three self-efficacy dimensions, we know that robust self-efficacy development might occur when multiple motivational types work synergistically rather than in isolation (Calkins et al., 2024). These interactive effects can create upward spirals where experiences in one domain reinforce motivation and self-efficacy in others.

2. Research Methods and Design

This study used a cross-sectional survey design to investigate the motivational antecedents influencing the adoption of AI among Ethiopian student teachers enrolled in Kotebe University of Education (Cohen et al., 2017). The design allowed for the collection of data at a single point in time, facilitating the comprehensive assessment of relationships between motivational factors and self-efficacy.
The study population comprised Ethiopian student teachers, i.e., preservice teachers enrolled in Kotebe University of Education in the year 2024/2025. The inclusion criteria required participants to be actively engaged in their studies and to have completed at least one semester of coursework related to subject-area methodologies. The final year of the teacher education program at Kotebe University of Education comprises two semesters. In the first semester, student teachers are given opportunities to learn and practice the teaching of subject-area content, including a school placement for classroom observation and guided teaching. In the second semester, student teachers engage in independent teaching for an extended period. Therefore, completion of the first semester of final-year coursework in teacher education indicates that student teachers have had sufficient exposure to planning, managing, and conducting teaching involving AI, enabling the assessment of their self-efficacy in using AI in classroom-based teaching and related factors. Individuals on academic probation or those who had previously received training in AI technologies were excluded from participation. A target sample size of approximately 290 participants was established based on power analysis calculations, referencing a prior study by (Subaveerapandiyan et al., 2025), which indicated an 87.5% likelihood of continued use of AI for academic purposes. We set a 95% confidence level, a 5% margin of error, and anticipated a 60% response rate. Ultimately, 278 complete questionnaires were gathered through convenience sampling, aimed at student teachers at Kotebe University of Education, who came from various regions of the country.
To secure an optimal sample size, all student teachers enrolled in the University programs who met the eligibility criteria described above were informed about the study in their respective classes and invited to the University Hall to complete the questionnaire. Upon arrival, they received a more detailed orientation, provided informed consent, and then completed the questionnaire. As each participant submitted a questionnaire, two of the authors immediately checked it for completeness; fully completed questionnaires were retained, while incomplete ones were excluded on the spot. The University administration and the heads of the various departments were informed about the study and facilitated the recruitment and gathering of respondents.
Data was collected using a self-administered questionnaire organized under seven constructs (four motivational factors: affective commitment, intrinsic motivation, extrinsic motivation, and altruistic motivation; and three self-efficacy-related factors: teaching AI skills, classroom management, and the student affective domain). The constructs and the corresponding measuring scales were adopted from different but relevant sources, as shown in Table 1.
Table 1. Constructs along with the sources.
Table 1. Constructs along with the sources.
ConstructSource
Self-efficacy-related construct
 Teaching AI skills Carolus et al. (2023)
 Planning and Classroom Management Carolus et al. (2023)
 The student affective domain Carolus et al. (2023)
Motivation-related constructs
 Intrinsic motivationArcher (1994)
 Extrinsic motivationDeci and Ryan (1980)
 Altruistic motivationRoness (2011)
 Affective commitmentMeyer et al. (1993)
Note: See Table 2 for the details of the items.
Table 2. Indicators.
Table 2. Indicators.
Indicator Source
Self-efficacy in using AI in teaching: Teaching AI Skills (TAIS)
1. I know how to teach students to use AI.
2. I can teach students to understand AI.
3. I can prepare students to keep up with the latest innovations in AI applications.
4. I can prepare students to detect AI.
5. I know how to discuss ethical aspects of AI with students.
6. I can teach students how to solve problems using AI.
7. I am able to teach students how to use AI in decision-making processes. 		Inspired by Carolus et al. (2023)
Self-efficacy in using AI in teaching: Planning and classroom management (PCM)
8. I can use AI to create lesson plans.
9. I know how to prevent students from being distracted when using AI.
10. I can prevent students from cheating using AI.
11. I can identify the knowledge students need when interacting with AI.
12. I know how to use AI to create multiple choice questions.
13. I can use AI to create learning material adapted to the actual student group.
14. I know how to use AI in student assessment.		Inspired by Carolus et al. (2023)
Self-efficacy in using AI in teaching: Student affective domain (SAD)
15. I know how to strengthen students’ confidence in using AI.
16. I know how to engage students to use AI for learning purposes.
17. I can make students excited about using AI for learning purposes.
18. I can prepare students to control their possible frustrations while using AI.
19. I can make the students control the euphoria that may arise when using AI.		Inspired by Carolus et al. (2023)
Affective commitment to the teaching profession (AC)
20. I feel attracted to the teaching profession.
21. It feels good to think that one day I will be a teacher.
22. I am looking forward to working as a teacher.		Inspired by Meyer et al. (1993)
Intrinsic motivation (IM)
23. I want to be a teacher because it is exciting to teach.
24. For me, it is a pleasure to interest students in my subject.
25. I want to be a teacher because I want others to be interested in learning.		Inspired by Archer (1994)
Extrinsic motivation (EM)
26. It is important to me to be looked up to by other student teachers.
27. It is important to me to be described as the best in the study group.		Inspired by Deci and Ryan (1980)
Altruistic motivation (AM)
28. It is important to me to work with people.
29. It is important to me to help people who need help.
30. For me, it is a pleasure to interest students in my subject.
31. For me, it is a pleasure to help others.		Inspired by Roness (2011)
Upon completion of data collection, the responses were systematically captured and entered into the IBM SPSS v30 statistical software package. The data were checked for completeness, and any inconsistencies or inaccuracies were addressed before analysis began. Descriptive statistics were calculated to summarize the demographic characteristics of participants, while structural equation modeling (SEM) was employed to examine the relationships between the variables of interest (Kline, 2023). Statistically significant path coefficients were interpreted to gauge the strength of associations between motivational factors and self-efficacy dimensions.
The sum score approach used in this study is a common technique in SEM that aggregates multiple observed indicators into a single composite score for a latent variable (Schuberth et al., 2025). This method simplifies analysis and interpretation while allowing researchers to explore complex relationships among the constructs in each model. The sum score approach is used to construct composite scores that summarize the relationships among multiple observed variables or indicators within a model (Kline, 2023). It involves creating a composite score by summing or averaging multiple items that measure a particular latent construct (an unobservable variable). For example, if a construct such as ‘extrinsic motivation’ is measured by several items on a questionnaire, the individual responses to those items can be summed to create a single score that represents the overall level of motivation for each participant. In SEM, researchers often use latent variables to represent constructs that cannot be directly measured but can be estimated based on several observed indicators. By aggregating responses from several indicators, the sum score provides a more comprehensive measure of the underlying construct, effectively capturing its multidimensional nature. Using sum scores as observed variables in the SEM can simplify the model structure and make analysis more straightforward, especially when the number of items is high (Kline, 2023).
We chose a set of items that are believed to represent the latent construct. For example, if measuring ‘self-efficacy in using AI in teaching, items related to specific contexts of self-efficacy (e.g., teaching, AI use, classroom management) may be selected. Responses to each item were given on a seven-point scale. The scores for the chosen items were summed to create the composite score. By consolidating several variables into a single score, researchers can reduce the complexity of their SEM models, which can improve model fit and computational efficiency. Composite scores enable comparisons across different groups or conditions by providing a single score reflective of the overall construct.

3. Results

Descriptive Statistics

Student teachers were asked to voluntarily complete a questionnaire. A total of 278 complete questionnaires were returned and analyzed. Prospective teachers answered questions on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree). The concepts were measured using two to seven individual items. An overview of the constructs, abbreviations, and items for the independent and dependent variables is provided in Table 2. Table 3 presents the characteristics in the sum scores of each variable. Table 4 shows bivariate correlations (Person r). All calculations are based on N = 278.
The above statistical description reveals a generally positive perception towards motivational factors influencing teaching efficacy using AI among student teachers, with the mean scores ranging approximately from 4.22 (PCM) to 4.80 (AM) with SDs of 1.58 and 1.85, respectively. All skewness and kurtosis values are negative, suggesting that the majority of the respondents rated motivational factors and their teaching efficacy positively, while extreme values are less likely to indicate relative homogeneity in the response.
As indicated above, the study used a single-step sum score approach in SEM to analyze the data. This decision was made with careful consideration of both the sample size and the minimum index thresholds used to determine model fit. According to Hair et al. (2010), a minimum sample size of 300 is required to conduct SEM when the number of latent constructs is ≤7, and each construct has ≥3 measuring items. In this study, the sample size of 278 was therefore judged to be more appropriate for a sum-score SEM approach, as the model included seven latent constructs, and one of these had fewer than three measurement items.
Regarding fit indices, a Chi-square value (X2) > 0.05 or relative Chi-square test (Chi-square/df) with a value of <3 is considered an acceptable fit (Kline, 2023). In addition, values greater than 0.90 on the Comparative Fit Index (CFI) and the Normed Fit Index (NFI) are typically regarded as the minimum requirement for acceptable model fit (Bentler & Bonett, 1980). Furthermore, a Root Mean Square Error of Approximation (RMSEA) value below 0.05 is preferred, although values up to 0.08 can be tolerated (MacCallum et al., 1996). MacCallum et al. go on to propose a Standardized Root Mean Square Residual (SRMR) value <0.05 as a good fit, where an SRMR of 0.09 can also be considered an adequate fit (MacCallum et al., 1996). In view of this, the current model qualified as a good fit to the data, with the X2/df = 2.02 (though the Chi/square test of the exact fit yielded a value of X2 (56) = 113.578 p < 0.001), CFI = 0.972, NFI = 0.946, and RMSEA = 0.063. The outcome of the 12 hypotheses tested is presented in Table 5.
As can be seen from Table 5 (regression weights) and Figure 3 (SEM with standardized regression weights), five out of twelve hypotheses were supported. We found quite a strong and positive relationship between affective commitment to the teaching profession (AC) and the student affective domain (SAD) (β = 0.51, p < 0.01), between affective commitment to the teaching profession (AC) and teaching AI skills (TAIS) (β = 0.40, p < 0.01) and between affective commitment to the teaching profession (AC) and self-efficacy in planning and classroom management (PCM) (β = 0.56, p < 0.01). We also found a moderate and positive association between extrinsic motivation and the student affective domain (β = 0.23, p < 0.05). Similarly, we found a moderate association between extrinsic motivation and teaching AI skills (β = 0.24, p < 0.05).
We found no statistically significant relationships between intrinsic motivation to the teaching profession (IM) and the endogenous variables for planning and classroom management (PCM) (β = 0.03, p > 0.05), student affective domain (β = 0.03, p > 0.05) or teaching AI skills (β = 0.11, p > 0.05). We also found no statistically significant associations between altruistic motivation to the teaching profession and the three endogenous variables for teaching AI skills (β = −0.11, p > 0.05), planning and classroom management (PCM) (β = 0.04, p > 0.05), and student affective domain (β = −0.04, p > 0.05). Finally, we found that the exogenous variables were positively correlated (see Figure 3).

4. Discussion

This study has explored the motivational factors, such as long-term commitment to teaching, along with intrinsic, extrinsic and altruistic motives’ influence on Ethiopian student teachers’ self-efficacy in adopting AI in their teaching practices. Accordingly, the study has yielded significant insights into the relationship between these motivations and student teachers’ self-efficacy across the key classroom teaching domains, including teaching AI skills (tais), student affective domain (sad) and planning and classroom management (pcm).
The results from the structural equation model indicate that Student teachers’ commitment to the teaching profession is one of the key variables positively influencing student teachers’ self-efficacy in adopting AI in teaching. This suggests that student teachers with higher commitment to the teaching profession tend to experience an elevated self-efficacy in adopting AI in their teaching, including efficacy of teaching AI skills, inspiring and engaging students in the learning process, and efficacy in planning and managing the classroom. In this case, student teachers with high affective commitment (emotional attachment) to the teaching profession tend to hold stronger personal values related to teaching. That is, when student teachers attribute high personal value to teaching, they are more likely to form an emotional bond with the profession and to develop professional identity as a teacher. This finding reinforces the earlier research conducted on the development of teachers’ self-efficacy based on Schwartz’s Theory of Human Values, which suggests that individuals with high personal values (such as security, social status, success, independence, kindness and pleasure) tend to possess higher self-efficacy (Barni et al., 2019; Octoria et al., 2024). The key message here is that when student teachers develop affective commitment to the teaching profession, they are more inclined to invest time and energy into their professional growth, which in turn helps them develop a positive feeling about their ability to adopt innovative methods and tools (Moses et al., 2021).
Student teachers with high personal values, as defined by Schwartz’s Theory of Human Values, are characterized by holistic development. They tend to be intellectually capable, emotionally balanced, able to maintain a healthy work–life balance, socially responsible, enjoy good social status, feel satisfied with themselves and their work, and demonstrate a strong commitment to the betterment of others, particularly younger generations (Sankar, 2025). Research also indicates that the use of AI promotes the holistic development of student teachers by providing access to support and learning resources, helping them balance their efforts, and improving their performance and efficiency (Barbieri & Nguyen, 2025). Though this study did not collect data to assess the holistic development of student teachers in relation to their self-efficacy, which can be an area for further investigation, it is evident that holistic development can enhance student teachers’ self-efficacy in using AI in their teaching.
However, the critical issue is the mechanisms by which this affective commitment to teaching can be cultivated in student teachers so that they can confidently adopt AI in their practices and facilitate holistic education. A growing body of literature from developed countries suggests that this can be addressed through programs that incorporate immersive learning experiences, community engagement, and mentorship into teacher training frameworks (Klassen et al., 2013). These elements help reinforce a sense of belonging and purpose among prospective teachers. For example, research by Eriksen et al. (2024) shows the possibility of shaping professional identity in the early days of teacher education through providing a week-long intensive professional development program addressing program goals, content, social networks, and expectations to meet in the program to be a successful teacher. Additionally, integrating AI applications into teacher training enhances the AI knowledge and skills of student teachers, which in turn boosts their perceived usefulness of AI, technical feasibility, and efficiency (C. Chan, 2024). This progression enhances their self-efficacy in adopting AI in teaching (Du & Gao, 2022). Moreover, fostering a sustainable sense of belonging to the teaching profession is deeply rooted in the social status that teachers and the profession itself enjoy (Richter et al., 2021; Semela, 2014). Meeting this demand requires a transformation in the teacher education ecosystem to attract aspiring teachers who are interested in teaching work and adapting AI tools in their teaching practices.
The moderate correlations observed between extrinsic motivation and self-efficacy underscore the significance of external factors, such as recognition and opportunities for career advancement, as influential in the adoption of AI tools. This finding supports existing literature informed by the Self-Determination Theory of Motivation, which emphasizes the role of extrinsic motivation in fostering innovative behavior and self-efficacy (Ahmadi et al., 2023; Klaeijsen et al., 2018). Moreover, the study of teachers’ motivation in the context of developing countries shows the relevance of extrinsic motivation in attracting student teachers to the teaching task (Han & Yin, 2016), which also contributes to their self-efficacy in adopting AI. Student teachers can extrinsically be motivated to explore technological tools and innovations when they perceive that these tools are under their control and easy to use (Calkins et al., 2024). They are also motivated when they believe that technology can enhance proficiency and effectiveness in planning instruction, delivering lessons, and managing the learning environment while reducing labor and effort (Şimşek et al., 2025). This requires them to approach and experience, or at least perceive, that using AI in teaching helps them to be more efficient in lesson planning, presentation, and engaging students in the learning process. According to Johnakin-Putnam (2020), such perceptions attract teachers to use technology, and they subsequently develop positive self-efficacy once they perceive its benefits and try it out.
Extrinsic motivation can be effective in initiating the development of self-efficacy, though the effect may not stay longer unless it transforms itself into internally oriented motives, such as working for excellence and the desire to achieve independence (Chiu et al., 2026). Thus, educators may also need other sources of motivation to sustain a high sense of self-efficacy in using technology emanated from extrinsic motives. Therefore, balancing the use of intrinsic and extrinsic motivational strategies is relevant to ensuring that educators find personal meaning and satisfaction in their roles, in addition to pursuing external recognition (Johnakin-Putnam, 2020). In this way, as an extrinsic factor, intentionally structuring and providing teacher–educator-led AI use in teacher education promotes stress management, brings efficiency, and enhances well-being among student teachers (Barbieri & Nguyen, 2025), which can be developed as a habit and sustained in later practice as a teacher.
The findings indicating no significant relationship for intrinsic and altruistic motivations for teaching suggest a need for further investigation into these areas. While intrinsic and altruistic motivations are important for engagement and passion in teaching (Bergmark et al., 2018; Nesje et al., 2018; Simonsz et al., 2023), their lack of impact on self-efficacy could reflect a disconnect between what motivates prospective teachers personally and the practical demands of teaching with AI. As such, future research could benefit from exploring how to better align intrinsic and altruistic motivations with the realities of the classroom, particularly in the context of integrating technology. Understanding the role of these motivational factors in teaching—especially in the context of student engagement with AI—could offer valuable insights into how teachers can better facilitate student learning and emotional needs (Roness, 2011). Put differently, future studies should examine whether intrinsic and altruistic motives truly have no impact, or which specific aspects of these motives are most strongly associated with gains in technology-related self-efficacy, and through which mechanisms these motivations can be leveraged to strengthen student teachers’ confidence in adopting AI in teaching.
Considering these findings, teacher education programs could prioritize nurturing a strong sense of commitment among student teachers while also fostering environments that encourage continuous professional development and adaptability to technological advancements. Programs should leverage the strengths of motivated individuals and provide avenues for them to deepen their engagement with both pedagogical practices and the integration of AI technologies (Abie & Serpa, 2019). Thus, the emphasis of the findings on the significance of student teachers’ external motivation and long-term commitment to the teaching profession carries several important implications for future teachers’ preparation. In the first case, teacher education programs could prioritize selecting individuals who exhibit a strong long-term commitment to teaching (Klassen et al., 2013). This focus can help ensure that new educators are not only passionate about the profession but also likely to persist in having higher self-efficacy in adopting AI in their future work. Teacher Education programs might consider developing targeted recruitment strategies that highlight the value of teaching and the impact it has on students and communities, appealing to those with a genuine passion for education. In addition, understanding that long-term commitment is a predictive factor for AI self-efficacy in teaching suggests that teacher education programs might benefit from incorporating elements that foster this commitment (Klassen et al., 2013). By providing prospective teachers with immersive experiences in classrooms, mentorship opportunities and connections to the educational community, programs can strengthen candidates’ attachment to the profession and enhance their likelihood of staying in the field (Eriksen et al., 2024).

5. Conclusions

This study has critically examined the motivational factors influencing Ethiopian student teachers’ self-efficacy in adopting AI in educational settings. The findings highlight the significant role of long-term commitment to the teaching profession, as well as intrinsic, extrinsic and altruistic motivations, in shaping self-efficacy related to teaching AI skills, classroom management and the student affective domain. Strong positive associations were observed between commitment to teaching and various dimensions of AI self-efficacy, indicating that student teachers who are deeply dedicated to their professional development are more likely to demonstrate high self-efficacy in integrating AI into their pedagogical practices and promote holistic education. The moderate influence of extrinsic motivation underscores the importance of external factors as drivers of self-efficacy in teaching with AI. In contrast, the lack of significant relationships for intrinsic and altruistic motivations suggests that further exploration of these factors is necessary to fully understand their potential impact on teaching practices. Given these insights, teacher education programs in Ethiopia and beyond should focus on nurturing long-term commitment among future educators. Understanding why motivation precedes efficacy has crucial implications for teacher preparation. It suggests that efforts to build strong teaching efficacy should begin with attention to affective commitment to the teaching profession and extrinsic motivation. The motivational foundation determines how experiences translate into efficacy beliefs. For teacher educators, this understanding emphasizes the importance of creating conditions that support affective commitment and extrinsic motivation. Implementing strategies that enhance student teachers’ engagement and providing ongoing support and mentorship will be crucial in fostering a workforce capable of effectively deploying AI in education. By prioritizing motivational factors that enhance self-efficacy, teacher training programs can better prepare student teachers to navigate the challenges of a rapidly changing educational landscape, ultimately contributing to improved teaching quality and student learning outcomes.

6. Limitations

While the sum score approach can be effective, it does have certain limitations (Schuberth et al., 2025). Aggregating items into a single score can obscure variations in individual item responses, potentially leading to a loss of nuanced information regarding the latent construct. Sum scores also assume that each item is equally important and contributes the same weight to the overall construct. Finally, summing responses can amplify measurement error if individual items do not reliably measure the intended construct.
The study employs a cross-sectional survey design, which captures data at a single point in time. This approach limits the ability to make causal inferences about the relationships between motivational factors and self-efficacy (Cohen et al., 2017). The sample was drawn from a single university, which may not provide a representative picture of all Ethiopian student teachers. This sampling method can introduce bias, as it may exclude students from underrepresented regions or those attending different types of teacher education institutions. As a result, the findings may not be generalizable to the broader population of student teachers in Ethiopia. The data were collected through self-reported questionnaires, which are subject to response biases such as social desirability, self-perception, and recall bias. Further, the study’s findings are situated within the specific socio-cultural and educational context of Ethiopia. The results may not readily translate to other cultural or educational settings, limiting the external validity of the findings. Different countries may have different educational systems, teaching philosophies, and levels of technological integration. Therefore, systematically focusing future research on the influence of motivational factors from student teacher recruitment through training across multi-institutional settings can compensate for the current study by clearly showing how motivation for the teaching profession influences teachers’ self-efficacy in adopting AI in teaching.

Author Contributions

All authors contributed significantly to this manuscript. Conceptualization, E.E., A.T. and A.B.H.; methodology, A.T.; software, K.-A.A.C.; validation, E.E., A.T., A.B.H. and K.-A.A.C.; formal analysis, K.-A.A.C.; investigation, E.E., and A.B.H.; data curation, A.B.H., F.T.G. and E.A.D.; writing—original draft preparation, E.E., A.B.H. and A.T.; writing—review and editing, A.B.H., E.E., A.T., K.-A.A.C., F.T.G. and E.A.D.; visualization, K.-A.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was exempt from formal ethical committee review in accordance with the institutional research guidelines of Kotebe University of Education. The confirmation of this exception was provided in a letter (Ref. RPD-2011/2026) dated 11 March 2026.

Data Availability Statement

The data generated and analyzed during this study are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to thank colleagues at Humboldt-Universität zu Berlin for discussions on the development of the Norwegian and German versions of the questionnaire. We are also grateful to Lemessa Abdi and Zelalem Sisay for coding the collected questionnaire and entering the data into SPSS. Additionally, we thank the faculty and staff at University of Oslo for their encouragement throughout the research process. Lastly, we are grateful to all the student teachers who participated in this study, as their contributions were essential for this research.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
SEMStructural equation modeling
TAISTeaching AI skills
PCMPlanning and classroom management
SADStudent affective domain
ACAffective commitment to teaching profession
IMIntrinsic Motivation
EMExtrinsic Motivation
AMAltruistic Motivation

References

  1. Abie, A., & Serpa, S. (2019). Post graduate diploma in teaching (PGDT) Pre-service teacher development policy reform initiative: Its Practices, outcomes and challenges in bahr dar university (BDU). Cogent Education, 6(1), 1696503. [Google Scholar] [CrossRef]
  2. Ahmadi, A., Noetel, M., Parker, P., Ryan, R. M., Ntoumanis, N., Reeve, J., Beauchamp, M., Dicke, T., Yeung, A., Ahmadi, M., Bartholomew, K., Chiu, T. K. F., Curran, T., Erturan, G., Flunger, B., Frederick, C., Froiland, J. M., González-Cutre, D., Haerens, L., … Lonsdale, C. (2023). A classification system for teachers’ motivational behaviors recommended in self-determination theory interventions. Journal of Educational Psychology, 115(8), 1158–1176. [Google Scholar] [CrossRef]
  3. Archer, J. (1994). Achievement goals as a measure of motivation in university students. Contemporary Educational Psychology, 19(4), 430–446. [Google Scholar] [CrossRef]
  4. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. [Google Scholar] [CrossRef]
  5. Barbieri, W., & Nguyen, N. (2025). Generative AI as a “placement buddy”: Supporting pre-service teachers in work-integrated learning, self-management and crisis resolution. Australasian Journal of Educational Technology, 41(2), 34–49. [Google Scholar] [CrossRef]
  6. Barni, D., Danioni, F., & Benevene, P. (2019). Teachers’ self-efficacy: The role of personal values and motivations for teaching. Frontiers in Psychology, 10, 1645. [Google Scholar] [CrossRef]
  7. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. [Google Scholar] [CrossRef]
  8. Bergmark, U., Lundström, S., Manderstedt, L., & Palo, A. (2018). Why become a teacher? Student teachers’ perceptions of the teaching profession and motives for career choice. European Journal of Teacher Education, 41(3), 266–281. [Google Scholar] [CrossRef]
  9. Bower, M., Torrington, J., Lai, J. W. M., Petocz, P., & Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29, 15403–15439. [Google Scholar] [CrossRef]
  10. Brandão, A., Pedro, L., & Zagalo, N. (2024). Teacher professional development for a future with generative artificial intelligence—An integrative literature review. Digital Education Review, (45), 151–157. [Google Scholar] [CrossRef]
  11. Burić, I., Jakšić, K., & Balaž, B. (2024). Teacher self-efficacy and teaching quality: A three-wave longitudinal investigation. International Journal of Psychology, 59(6), 1317–1325. [Google Scholar] [CrossRef]
  12. Calkins, L., Wiens, P., Parker, J., & Tschinkel, R. (2024). Teacher motivation and self-efficacy: How do specific motivations for entering teaching relate to teacher self-efficacy? Journal of Education, 204(2), 427–438. [Google Scholar] [CrossRef]
  13. Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS—Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. [Google Scholar] [CrossRef]
  14. Chan, C. (2024). Holistic competencies and AI in education: A synergistic pathway. Australasian Journal of Educational Technology, 40(5), 1–12. [Google Scholar] [CrossRef]
  15. Chan, S., Maneewan, S., & Koul, R. (2023). Teacher educators’ teaching styles: Relation with learning motivation and academic engagement in pre-service teachers. Teaching in Higher Education, 28(8), 2044–2065. [Google Scholar] [CrossRef]
  16. Chang, T.-J., & Sung, Y.-T. (2024). Does teacher motivation really matter? Exploring the mediating role of teachers’ self-efficacy in the relationship between motivation and job satisfaction. The Asia-Pacific Education Researcher, 33(6), 1315–1325. [Google Scholar] [CrossRef]
  17. Chiu, T. K. F., Bali, S., Tondeur, J., Howard, S., & Chan, K. K. H. (2026). Teacher education for artificial intelligence literacy through a self-determination theory perspective. European Journal of Teacher Education, 1–21. [Google Scholar] [CrossRef]
  18. Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th ed.). Routledge. [Google Scholar] [CrossRef]
  19. Darling-Hammond, L., Hyler, M. E., & Wojcikiewicz, S. (2025). Design principles for teacher preparation: Enacting the science of learning and development. Learning Policy Institute. [Google Scholar] [CrossRef]
  20. Deci, E. L. (1971). Effects of externally mediated rewards on instrinsic motivation. Journal of Personality and Social Pschology, 18(1), 105–115. [Google Scholar] [CrossRef]
  21. Deci, E. L., & Ryan, R. M. (1980). Self-determination theory: When mind mediates behavior. The Journal of Mind and Behavior, 1(1), 33–43. Available online: https://www.jstor.org/stable/43852807 (accessed on 10 March 2025).
  22. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. [Google Scholar] [CrossRef]
  23. Ding, L.-J., Li, J.-M., & Hui, B.-H. (2025). Will teacher-AI collaboration enhance teaching engagement? Behavioral Sciences, 15(7), 866. [Google Scholar] [CrossRef] [PubMed]
  24. Du, Y., & Gao, H. (2022). Determinants affecting teachers’ adoption of AI-based applications in EFL context: An analysis of analytic hierarchy process. Education and Information Technologies, 27(7), 9357–9384. [Google Scholar] [CrossRef]
  25. Eriksen, A., Leming, T., & Skrøvset, S. (2024). Shaping professional identity in the early days of teacher education. In Transforming university-based teacher education through innovation: A Norwegian response to research literacy, integration and technology (1st ed., pp. 197–204). Routledge. [Google Scholar] [CrossRef]
  26. Friedman, I. A., & Kass, E. (2002). Teacher self-efficacy: A classroom-organization conceptualization. Teaching and Teacher Education, 18(6), 675–686. [Google Scholar] [CrossRef]
  27. Garvis, S., & Pendergast, D. (2011). An investigation of early childhood teacher self-efficacy beliefs in the teaching of arts education. International Journal of Education & the Arts, 12(9). Available online: http://www.ijea.org/v12n9/ (accessed on 20 March 2025).
  28. Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Cluster analysis. In J. Hair, W. Black, & B. Babin (Eds.), Multivariate data analysis: A global perspective (7th ed., pp. 477–536). Pearson Prentice Hall. [Google Scholar]
  29. Han, J., & Yin, H. (2016). Teacher motivation: Definition, research development and implications for teachers. Cogent Education, 3(1), 1217819. [Google Scholar] [CrossRef]
  30. Hazzan-Bishara, A., Kol, O., & Levy, S. (2025). The factors affecting teachers’ adoption of AI technologies: A unified model of external and internal determinants. Education and Information Technologies, 30(11), 15043–15069. [Google Scholar] [CrossRef]
  31. Hußner, I., Lazarides, R., & Symes, W. (2024). The relation between self-efficacy and teaching behaviour: A video-based analysis of student teachers. Learning and Instruction, 91, 101880. [Google Scholar] [CrossRef]
  32. IBM. (2023). IBM global AI adoption index report dec. 2023. IBM Morning Consult. [Google Scholar]
  33. Johnakin-Putnam, S. (2020). The impact of teacher intrinsic and extrinsic motivation on teacher self-efficacy [Ph.D. Thesis, Liberty University]. Available online: https://digitalcommons.liberty.edu/cgi/viewcontent.cgi?article=3677&context=doctoral (accessed on 10 December 2025).
  34. Klaeijsen, A., Vermeulen, M., & Martens, R. (2018). Teachers’ innovative behaviour: The importance of basic psychological need satisfaction, intrinsic motivation, and occupational self-efficacy. Scandinavian Journal of Educational Research, 62(5), 769–782. [Google Scholar] [CrossRef]
  35. Klassen, R., Wilson, E., Siu, A. F. Y., Hannok, W., Wong, M. W., Wongsri, N., Sonthisap, P., Pibulchol, C., Buranachaitavee, Y., & Jansem, A. (2013). Preservice teachers’ work stress, self-efficacy, and occupational commitment in four countries. European Journal of Psychology of Education, 28(4), 1289–1309. [Google Scholar] [CrossRef]
  36. Kline, R. B. (2023). Structural equation modeling in neuropsychology research. In G. G. Brown, B. Crosson, K. Y. Haaland, & T. Z. King (Eds.), APA handbook of neuropsychology, Volume 2: Neuroscience and neuromethods (Vol. 2, pp. 681–698). American Psychological Association. [Google Scholar] [CrossRef]
  37. Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. [Google Scholar] [CrossRef]
  38. Li, B., Lowell, V. L., Wang, C., & Li, X. (2024). A systematic review of the first year of publications on ChatGPT and language education: Examining research on ChatGPT’s use in language learning and teaching. Computers and Education: Artificial Intelligence, 7, 100266. [Google Scholar] [CrossRef]
  39. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. [Google Scholar] [CrossRef]
  40. Margaryan, A. (2023). Artificial intelligence and skills in the workplace: An integrative research agenda. Big Data & Society, 10(2). [Google Scholar] [CrossRef]
  41. Martínez-Moreno, J., & Petko, D. (2024). What motivates future teachers? The influence of artificial intelligence on student teachers’ career choice. Computers and Education: Artificial Intelligence, 7, 100296. [Google Scholar] [CrossRef]
  42. Mekonnen, D. M. (2008). Reflections on the teacher education system overhaul (TESO) program in Ethiopia: Promises, pitfalls, and propositions. Journal of Educational Change, 9(3), 281–304. [Google Scholar] [CrossRef]
  43. Menon, D., Wieselmann, J. R., Haines, S., & Asim, S. (2024). A meta-synthesis of the literature on science & engineering teaching self-efficacy: Current gaps and future research directions. Journal of Science Teacher Education, 35(5), 480–503. [Google Scholar] [CrossRef]
  44. Meyer, J. P., Allen, N. J., & Smith, C. A. (1993). Commitment to organizations and occupations: Extension and test of a three-component conceptualization. Journal of Applied Psychology, 78(4), 538–551. [Google Scholar] [CrossRef]
  45. Michael, K. (2025). Teacher education curriculum framework of Ethiopia: Technology integration issues and reflection for improvement. Ethiopian Journal of Education and Sciences, 20(2), 20–36. Available online: https://journals.ju.edu.et/index.php/ejes/article/view/6331 (accessed on 10 December 2025).
  46. Ministry of Education. (2021). Ethiopian education sector development program 6 (ESDP VI). Ministry of Education. [Google Scholar]
  47. Miseliunaite, B., Kliziene, I., & Cibulskas, G. (2022). Can holistic education solve the world’s problems: A systematic literature review. Sustainability, 14(15), 9737. [Google Scholar] [CrossRef]
  48. Moses, I., Admiraal, W., Berry, A., & Saab, N. (2021). Teacher education and student teachers’ commitment to teaching: A thematic literature review. Africa Education Review, 18(3–4), 75–92. [Google Scholar] [CrossRef]
  49. Na, C., & Isa, Z. M. (2024). Exploring the influence of teacher self-efficacy on teaching quality in higher vocational education. Journal of Digitainability, Realism & Mastery (DREAM), 3(7), 16–27. [Google Scholar] [CrossRef]
  50. Nesje, K., Brandmo, C., & Berger, J.-L. (2018). Motivation to become a teacher: A Norwegian validation of the factors influencing teaching choice scale. Scandinavian Journal of Educational Research, 62(6), 813–831. [Google Scholar] [CrossRef]
  51. Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. [Google Scholar] [CrossRef]
  52. Octoria, D., Harahap, D. H., & Sari, P. R. (2024). The effect of teachers’ self-efficacy and teaching commitment on pre-service teachers’ performance. Multidisciplinary Science Journal, 7(4), 2025181. [Google Scholar] [CrossRef]
  53. Opoku, M. P., Belbase, S., Nketsia, W., Side, A. S., Were, D., & Gemeda, F. T. (2025). Comparative study of teachers’ attitudes, self-efficacy and job satisfaction when teaching gifted students in Ethiopia and Kenya. Compare: A Journal of Comparative and International Education, 1–18. [Google Scholar] [CrossRef]
  54. Perera, H. N., & John, J. E. (2020). Teachers’ self-efficacy beliefs for teaching math: Relations with teacher and student outcomes. Contemporary Educational Psychology, 61, 101842. [Google Scholar] [CrossRef]
  55. Richter, E., Lazarides, R., & Richter, D. (2021). Four reasons for becoming a teacher educator: A large-scale study on teacher educators’ motives and well-being. Teaching and Teacher Education, 102, 103322. [Google Scholar] [CrossRef]
  56. Roness, D. (2011). Still motivated? The motivation for teaching during the second year in the profession. Teaching and Teacher Education, 27(3), 628–638. [Google Scholar] [CrossRef]
  57. Roness, D., & Smith, K. (2009). Postgraduate Certificate in Education (PGCE) and student motivation. European Journal of Teacher Education, 32(2), 111–134. [Google Scholar] [CrossRef]
  58. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. [Google Scholar] [CrossRef] [PubMed]
  59. Sankar, V. (2025). Holistic development in education: Nurturing well—Rounded individuals for a complex world. Shanlax International Journal of Arts, Science and Humanities, 12(S2-Jan), 103–106. [Google Scholar] [CrossRef]
  60. Schuberth, F., Schamberger, T., Kemény, I., & Henseler, J. (2025). The sum score model: Specifying and testing equally weighted composites using structural equation modeling. Psychometrika, 90(1), 358–383. [Google Scholar] [CrossRef]
  61. Schwartz, S. H. (2012). An overview of the Schwartz theory of basic values. Online Readings in Psychology and Culture, 2(1). [Google Scholar] [CrossRef]
  62. Semela, T. (2014). Teacher preparation in Ethiopia: A critical analysis of reforms. Cambridge Journal of Education, 44(1), 113–145. [Google Scholar] [CrossRef]
  63. Simonsz, H., Leeman, Y., & Veugelers, W. (2023). On the threshold of being a teacher: Motivations and ideals of 116 Dutch student teachers. Asia-Pacific Journal of Teacher Education, 51(4), 328–343. [Google Scholar] [CrossRef]
  64. Subaveerapandiyan, A., Radhakrishnan, S., Tiwary, N., & Guangul, S. M. (2025). Student satisfaction with artificial intelligence chatbots in Ethiopian academia. IFLA Journal, 51(3), 600–614. [Google Scholar] [CrossRef]
  65. Şimşek, A. S., Cengiz, G. Ş. T., & Bal, M. (2025). Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI. Education and Information Technologies, 30(15), 20913–20942. [Google Scholar] [CrossRef]
  66. Tabernero, C., & Hernández, B. (2011). Self-efficacy and intrinsic motivation guiding environmental behavior. Environment and Behavior, 43(5), 658–675. [Google Scholar] [CrossRef]
  67. Tuli, F., Tekle, E., Hunde, A. B., Baye, G., Waktole, Y., Yifru, M., Abate, E., Bogal, B., Abate, S., Fentahun, T., & Beyene, F. (2025). Reimagining initial teacher education for the 21st Century in Ethiopia. Kotebe Journal of Education, 3(1), 89–118. [Google Scholar] [CrossRef] [PubMed]
  68. UNESCO. (2015). Rethinking education: Towards a global common good? UNESCO. [Google Scholar] [CrossRef]
  69. Woldemariam, M. T., Ergado, A. A., & Jimma, W. (2025). Factors influencing effective integration of educational technology in the colleges of teacher education in Ethiopia: A constructivist grounded theory. Australasian Journal of Educational Technology, 41(2), 50–70. [Google Scholar] [CrossRef]
  70. Yang, Y.-F., Tseng, C. C., & Lai, S.-C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, 104582. [Google Scholar] [CrossRef]
  71. Zhao, J., Li, S., & Zhang, J. (2025). Understanding teachers’ adoption of ai technologies: An empirical study from Chinese middle schools. Systems, 13(4), 302. [Google Scholar] [CrossRef]
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Figure 1. The underlying motivational theories of self-efficacy in adopting AI in classroom-based teaching.
Figure 1. The underlying motivational theories of self-efficacy in adopting AI in classroom-based teaching.
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Figure 2. The framework for motivational factor vs. self-efficacy in teaching using AI.
Figure 2. The framework for motivational factor vs. self-efficacy in teaching using AI.
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Figure 3. Structural equation modeling (SEM) (N = 278). Note: SEM based on the sum score approach: teaching AI skills (tais), planning and classroom management (pcm), student affective domain (sad), affective commitment to the teaching profession (ac), intrinsic motivation (im), extrinsic motivation (em), altruistic motivation (am), residual of tais (eta), residual of pcm (epc), and residual of sad (esa).
Figure 3. Structural equation modeling (SEM) (N = 278). Note: SEM based on the sum score approach: teaching AI skills (tais), planning and classroom management (pcm), student affective domain (sad), affective commitment to the teaching profession (ac), intrinsic motivation (im), extrinsic motivation (em), altruistic motivation (am), residual of tais (eta), residual of pcm (epc), and residual of sad (esa).
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Table 3. Descriptive statistics (N = 278): N (number of participants), minimum value (Min), maximum value (Max), mean, standard deviation (SD), skewness (Skew), kurtosis (Kurt).
Table 3. Descriptive statistics (N = 278): N (number of participants), minimum value (Min), maximum value (Max), mean, standard deviation (SD), skewness (Skew), kurtosis (Kurt).
MinMaxMeanSDSkewKurt
AC174.441.77−0.37−0.94
IM174.571.75−0.40−0.83
EM174.681.77−0.48−0.65
AM174.801.85−0.57−0.72
TAIS174.391.62−0.43−0.77
PCM174.221.58−0.39−0.65
SAD174.251.67−0.32−0.73
Note: The abbreviations in the leftmost column are defined in Table 2.
Table 4. Bivariate correlations (Pearson’s r).
Table 4. Bivariate correlations (Pearson’s r).
N = 278ACIMEMAMTAISPCM
IM0.68--
EM0.600.76--
AM0.540.640.77--
TAIS0.560.500.480.37--
PCM0.680.540.520.460.76--
SAD0.640.520.510.420.720.85
Note: The abbreviations are defined in Table 2. -- indicate that the value is not applicable as correlation with itself yield 1.
Table 5. Hypothesis test result, N = 278.
Table 5. Hypothesis test result, N = 278.
H. NoDirect PathsEstimate S.EC.R.pRemark
H1IM > TAIS0.1030.0761.3510.177Not supported
H2IM > PCM0.0270.0670.4020.688Not supported
H3IM > SAD0.0270.0740.3600.719Not supported
H4EM > TAIS0.2230.0832.670**Supported
H5EM > PCM0.1200.0731.6460.100Not supported
H6EM > SAD0.2120.0812.617**Supported
H7AM > TAIS−0.0960.067−1.4350.151Not supported
H8AM > PCM0.0350.0590.5900.555Not supported
H9AM > SAD0.0350.0590.5900.555Not supported
H10AC > TAIS0.3660.0615.998***Supported
H11AC > PCM0.4980.0539.324***Supported
H12AC > SAD0.4750.0598.018***Supported
Note: *** indicates significance at p < 0.01. ** indicates significance at p < 0.05. The abbreviations are defined in Table 2.
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Hunde, A.B.; Elstad, E.; Christophersen, K.-A.A.; Turmo, A.; Gemeda, F.T.; Demissie, E.A. Motivational Factors Influencing Ethiopian Student Teachers’ Self-Efficacy in Adopting AI in Education. Educ. Sci. 2026, 16, 800. https://doi.org/10.3390/educsci16050800

AMA Style

Hunde AB, Elstad E, Christophersen K-AA, Turmo A, Gemeda FT, Demissie EA. Motivational Factors Influencing Ethiopian Student Teachers’ Self-Efficacy in Adopting AI in Education. Education Sciences. 2026; 16(5):800. https://doi.org/10.3390/educsci16050800

Chicago/Turabian Style

Hunde, Adula Bekele, Eyvind Elstad, Knut-Andreas Abben Christophersen, Are Turmo, Fekede Tuli Gemeda, and Eyueil Abate Demissie. 2026. "Motivational Factors Influencing Ethiopian Student Teachers’ Self-Efficacy in Adopting AI in Education" Education Sciences 16, no. 5: 800. https://doi.org/10.3390/educsci16050800

APA Style

Hunde, A. B., Elstad, E., Christophersen, K.-A. A., Turmo, A., Gemeda, F. T., & Demissie, E. A. (2026). Motivational Factors Influencing Ethiopian Student Teachers’ Self-Efficacy in Adopting AI in Education. Education Sciences, 16(5), 800. https://doi.org/10.3390/educsci16050800

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