Behavioral Sciences

Research

25 pages, 543 KB

Open AccessArticle

A Latent Profile Analysis of Emotions in AI-Mediated IDLE: Associations with Emotion Regulation Strategies and Perceived AI Affordances

by Zihan Gao and Chenxi Du

Behav. Sci. 2026, 16(2), 283; https://doi.org/10.3390/bs16020283 - 15 Feb 2026

Viewed by 769

Abstract

The rapid development and easy accessibility of artificial intelligence (AI) technology have led to a significant rise in informal digital learning of English (IDLE). However, the emotional experiences across different cohorts of learners remain underexplored. Contextualized in AI-mediated IDLE, the present study integrated [...] Read more.

The rapid development and easy accessibility of artificial intelligence (AI) technology have led to a significant rise in informal digital learning of English (IDLE). However, the emotional experiences across different cohorts of learners remain underexplored. Contextualized in AI-mediated IDLE, the present study integrated the control-value theory of achievement emotions and the process model of emotion regulation to investigate the latent profiles of emotions and further examine their relations to emotion regulation strategies (cognitive reappraisal and expressive suppression) and perceived AI affordances. Questionnaires were administered to 613 English as a foreign language undergraduates in China. Latent profile analysis revealed three emotion profiles, including moderate positive and moderate negative emotions group (Profile 1, 43%); high positive and low negative emotions group (Profile 2, 21%); and high positive and high negative emotions group (Profile 3, 36%). The Bolck–Croon–Hagenaars (BCH) analysis indicated that students in Profile 2 scored the highest on perceived AI affordances, followed by those in Profile 3 and Profile 1. Additionally, multinomial logistic regression analysis showed that cognitive reappraisal was a stronger predictor of membership in Profiles 2 and 3 compared with Profile 1, while expressive suppression predicted membership in Profile 3 to the greatest extent, followed by Profiles 1 and 2. Pedagogical implications were provided to cultivate learners’ optimal emotional state. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

32 pages, 2250 KB

Open AccessArticle

Divergent Role of AI in Social Development: A Comparative Study of Teachers’ and Students’ Perceptions in Online and Physical Classrooms

by Qianye Wen, Jianliang Wang, Zhuoqi Guo and Daniel Badulescu

Behav. Sci. 2025, 15(12), 1649; https://doi.org/10.3390/bs15121649 - 30 Nov 2025

Viewed by 1414

Abstract

This study addresses a critical gap in understanding Artificial Intelligence (AI)’s role in education by empirically investigating and comparing the distinct perceptions of teachers and students regarding AI’s role in a comprehensive range of social development aspects in both online and physical classroom [...] Read more.

This study addresses a critical gap in understanding Artificial Intelligence (AI)’s role in education by empirically investigating and comparing the distinct perceptions of teachers and students regarding AI’s role in a comprehensive range of social development aspects in both online and physical classroom settings. In particular, we evaluated how teachers utilize AI in their teaching methods, namely, Communicative Language Teaching (CLT), the Direct Method (DL), Task-Based Language Teaching (TBLT), Content and Language Integrated Learning (CLIL), and Community Language Learning (CLL), and students in their learning methods, namely, Communicative Learning (CL), Immersive Learning (IL), Task-Based Collaborative Learning (TBCL), Content Integrated Learning (CIL), and Community-Based Reflective Learning (CBRL), to configure their social development. We interviewed 20 teachers (10 from online and 10 from physical classes) and 40 students (20 from online and 20 from physical classes) and evaluated their perceptions regarding AI usage in teaching and learning methods towards social development. The results of our study are convincing enough to suggest that both teachers and students perceive AI usage helpful in teaching models; however, variation in their perception is observed. Notably, the divergence in the perception of teachers and students with regard to AI’s role is a key observation of this study. For instance, the teachers perceived AI as a highly effective tool in fostering community building during online sessions; in contrast, the students viewed its role as being moderately effective. Likewise, the teachers perceived AI’s role as a critical tool in traditional classrooms rather than in virtual ones, whereas the students associated AI with online learning—in terms of digital tools, learning opportunities, and critical discussion—by rating its impact on social confidence and verbal–nonverbal communications significantly more strongly in physical settings. On the contrary, the teachers emphasized AI’s relevance to their self-confidence, emotional intelligence, and community engagement in online teaching platforms; yet, the ratings dropped to moderate in physical contexts. The students’ perceptions in this regard matched those of the teachers, as they also emphasized the importance of social confidence and overall well-being in physical classrooms, where the teachers’ assessment was comparatively low. These patterns provide analytical insights that are decisively valuable for designing AI-integrated pedagogical models that support social development within the educational environments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

17 pages, 485 KB

Open AccessArticle

Harnessing Self-Control and AI: Understanding ChatGPT’s Impact on Academic Wellbeing

by Metin Besalti

Behav. Sci. 2025, 15(9), 1181; https://doi.org/10.3390/bs15091181 - 29 Aug 2025

Cited by 1 | Viewed by 5041

Abstract

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and [...] Read more.

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

20 pages, 661 KB

Open AccessArticle

An Analysis of Students’ Attitudes Toward Artificial Intelligence—ChatGPT, in Particular—In Relation to Personality Traits, Coping Strategies, and Personal Values

by Simona Maria Glaveanu and Roxana Maier

Behav. Sci. 2025, 15(9), 1179; https://doi.org/10.3390/bs15091179 - 29 Aug 2025

Cited by 1 | Viewed by 2764

Abstract

The general objective of this research was to investigate the attitudes of Bucharest students toward artificial intelligence (AI)—in particular, ChatGPT—in relation to their personality traits, coping strategies, and personal values to identify psychosocial approaches for students’ effective reporting toward this AI product. As [...] Read more.

The general objective of this research was to investigate the attitudes of Bucharest students toward artificial intelligence (AI)—in particular, ChatGPT—in relation to their personality traits, coping strategies, and personal values to identify psychosocial approaches for students’ effective reporting toward this AI product. As there was no instrument validated and calibrated on Romanian students, the scale constructed by Acosta-Enriquez et al. in 2024 was adapted to students from Bucharest (N = 508). Following the item analysis, the adapted scale was reduced to 16 items, and, following the factor analysis (EFA–0.81 < α < 0.91), the structure with three factors (cognitive, affective, and behavioral components), explaining 53% of the variation in Bucharest students’ attitudes toward ChatGPT, was maintained considering the results of the confirmatory factor analysis—CFA (χ²(79) = 218.345, p < 0.001; CMIN/DF = 2.486; CFI = 0.911; TLI = 0.900; RMSEA = 0.058 (90% CI: 0.50–0.065). The present study showed that 85.53% of the research subjects used ChatGPT at least once, of which 24.11% have a positive/open attitude toward ChatGPT, and that there are correlations (p < 0.01; 0.23 < r² < 0.50) between students’ attitudes toward ChatGPT and several personality traits, coping strategies, and personal values. It also proves that the three components of the attitude toward ChatGPT (cognitive, affective, and behavioral) are correlated with a series of personality traits, coping strategies, and personal values of students. Although the general objective was achieved and the adapted scale has adequate psychometric qualities, the authors propose in future studies to expand the group of subjects so that the scale can be validated at the level of the Romanian population. In this research, at the end, several concrete approaches are proposed for the effective reporting of students toward this AI product, which, beyond the ethical challenges, also recognizes the benefits of technology in the evolution of education. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

23 pages, 854 KB

Open AccessArticle

Adopting Generative AI in Future Classrooms: A Study of Preservice Teachers’ Intentions and Influencing Factors

by Yang Liu, Qiu Wang and Jing Lei

Behav. Sci. 2025, 15(8), 1040; https://doi.org/10.3390/bs15081040 - 31 Jul 2025

Cited by 12 | Viewed by 7188

Abstract

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity [...] Read more.

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

17 pages, 1035 KB

Open AccessArticle

Whether and When Could Generative AI Improve College Student Learning Engagement?

by Fei Guo, Lanwen Zhang, Tianle Shi and Hamish Coates

Behav. Sci. 2025, 15(8), 1011; https://doi.org/10.3390/bs15081011 - 25 Jul 2025

Cited by 5 | Viewed by 3797

Abstract

Generative AI (GenAI) technologies have been widely adopted by college students since the launch of ChatGPT in late 2022. While the debate about GenAI’s role in higher education continues, there is a lack of empirical evidence regarding whether and when these technologies can [...] Read more.

Generative AI (GenAI) technologies have been widely adopted by college students since the launch of ChatGPT in late 2022. While the debate about GenAI’s role in higher education continues, there is a lack of empirical evidence regarding whether and when these technologies can improve the learning experience for college students. This study utilizes data from a survey of 72,615 undergraduate students across 25 universities and colleges in China to explore the relationships between GenAI use and student learning engagement in different learning environments. The findings reveal that over sixty percent of Chinese college students use GenAI technologies in Academic Year 2023–2024, with academic use exceeding daily use. GenAI use in academic tasks is related to more cognitive and emotional engagement, though it may also reduce active learning activities and learning motivation. Furthermore, this study highlights that the role of GenAI varies across learning environments. The positive associations of GenAI and student engagement are most prominent for students in “high-challenge and high-support” learning contexts, while GenAI use is mostly negatively associated with student engagement in “low-challenge, high-support” courses. These findings suggest that while GenAI plays a valuable role in the learning process for college students, its effectiveness is fundamentally conditioned by the instructional design of human teachers. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

13 pages, 961 KB

Open AccessArticle

Examining How Preschool Teachers’ Positive Psychological Capital Impacts Digital Education Innovation: A Moderated Moderation Analysis of Effort Expectancy and Behavioral Intention

by Myoung-Sun Sung and Young-Eun Lee

Behav. Sci. 2025, 15(7), 952; https://doi.org/10.3390/bs15070952 - 14 Jul 2025

Cited by 1 | Viewed by 1440

Abstract

This study examines the impact of preschool teachers’ positive psychological capital on their digital education innovation behavior, focusing on the moderated moderation effects of digital education effort expectancy and behavioral intention on this relationship. Data were analyzed from 211 preschool teachers of children [...] Read more.

This study examines the impact of preschool teachers’ positive psychological capital on their digital education innovation behavior, focusing on the moderated moderation effects of digital education effort expectancy and behavioral intention on this relationship. Data were analyzed from 211 preschool teachers of children aged 3–5 years in South Korea. SPSS (version 25.0) was used to conduct descriptive and Pearson correlation analyses, and PROCESS Macro (version 4.3) was used to perform the moderation analysis. The results indicate that preschool teachers with higher positive psychological capital exhibited increased innovation behavior toward digital education, and this effect was further strengthened by higher effort expectancy. These research findings can provide useful foundational data for designing teacher training programs to promote preschool teachers’ digital education innovation behavior. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

25 pages, 319 KB

Open AccessArticle

The Impact of Digital Learning Competence on the Academic Achievement of Undergraduate Students

by Yafeng Song, Shuqi Lv, Meng Wang, Zhuoxi Wang and Wei Dong

Behav. Sci. 2025, 15(7), 840; https://doi.org/10.3390/bs15070840 - 22 Jun 2025

Cited by 7 | Viewed by 9059

Abstract

Digital learning competence has gradually become one of the core qualities essential for undergraduate students. To effectively enhance undergraduates’ digital learning abilities and their positive impact on academic performance, this study developed a validated survey on digital learning competence and academic achievement. A [...] Read more.

Digital learning competence has gradually become one of the core qualities essential for undergraduate students. To effectively enhance undergraduates’ digital learning abilities and their positive impact on academic performance, this study developed a validated survey on digital learning competence and academic achievement. A total of 312 valid questionnaires were collected from undergraduate students. Descriptive statistical analysis revealed that the overall academic achievement of the sample students was at an upper-middle level, with course achievements and practical achievements being higher than scholarly achievements. Differential analysis showed that male students scored higher than female students in scholarly achievements, practical achievements, and overall academic performance. Additionally, senior students generally outperformed junior students in course achievement, academic research, and overall academic performance, while undergraduates from key universities generally achieved higher academic results than those from ordinary undergraduate institutions. Correlation and regression analyses indicated that digital learning evaluation competence as a sub-competence under digital learning competence has significant positive predictive effects on undergraduates’ academic achievement. When other factors remained constant, for each unit increase in digital learning evaluation ability, academic achievement increased by 0.480 units. Therefore, universities can improve existing student development processes through measures such as enriching carriers, optimizing methods, and creating supportive environments to foster undergraduates’ digital learning competence, thereby enhancing their academic achievement. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

22 pages, 314 KB

Open AccessArticle

AI as the Therapist: Student Insights on the Challenges of Using Generative AI for School Mental Health Frameworks

by Cecilia Ka Yuk Chan

Behav. Sci. 2025, 15(3), 287; https://doi.org/10.3390/bs15030287 - 28 Feb 2025

Cited by 24 | Viewed by 18896 | Correction

Abstract

The integration of generative AI (GenAI) in school-based mental health services presents new opportunities and challenges. This study focuses on the challenges of using GenAI chatbots as therapeutic tools by exploring secondary school students’ perceptions of such applications. The data were collected from [...] Read more.

The integration of generative AI (GenAI) in school-based mental health services presents new opportunities and challenges. This study focuses on the challenges of using GenAI chatbots as therapeutic tools by exploring secondary school students’ perceptions of such applications. The data were collected from students who had both theoretical and practical experience with GenAI. Based on Grodniewicz and Hohol’s framework highlighting the “Problem of a Confused Therapist”, “Problem of a Non-human Therapist”, and “Problem of a Narrowly Intelligent Therapist”, qualitative data from student reflections were examined using thematic analysis. The findings revealed that while students acknowledged AI’s benefits, such as accessibility and non-judgemental feedback, they expressed significant concerns about a lack of empathy, trust, and adaptability. The implications underscore the need for AI chatbot use to be complemented by in-person counselling, emphasising the importance of human oversight in AI-augmented mental health care. This study contributes to a deeper understanding of how advanced AI can be ethically and effectively incorporated into school mental health frameworks, balancing technological potential with essential human interaction. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

14 pages, 472 KB

Open AccessArticle

How Epistemic Curiosity Influences Digital Literacy: Evidence from International Students in China

by Shaojun Ma, Xuan Jin, Xin Li, Hongming Dong, Xuehang Dong and Bowen Tang

Behav. Sci. 2025, 15(3), 286; https://doi.org/10.3390/bs15030286 - 28 Feb 2025

Cited by 5 | Viewed by 2800

Abstract

Digital literacy is the core competitiveness and necessary ability that international students should cultivate while studying in China in the context of education digitalization, and this paper mainly explores whether epistemic curiosity can affect the digital literacy of international students in China. Based [...] Read more.

Digital literacy is the core competitiveness and necessary ability that international students should cultivate while studying in China in the context of education digitalization, and this paper mainly explores whether epistemic curiosity can affect the digital literacy of international students in China. Based on the Technology Acceptance Model, this paper introduces the variable of epistemic curiosity, uses questionnaire survey method and quantitative tools (SPSS and AMOS software) to construct a model of the cognition–perception–formation mechanism of international students’ digital literacy in China, and obtains the following conclusions: Firstly, both interest- and deprivation-type epistemic curiosity can directly promote the digital literacy of international students in China. Secondly, this paper discusses how interest- and deprivation-type epistemic curiosity can affect digital literacy under the mediating effect of perceived usefulness. Finally, perceived ease of use can also indirectly promote the relationship between epistemic curiosity and digital literacy of international students in China. The contribution of this paper is to highlight the formation mechanism of digital literacy in cross-cultural contexts and to explore how interest- and deprivation-type epistemic curiosity affect the digital literacy of international students in China. To a certain extent, this paper reveals the potential process of international students in China to use digital resources to transform into digital literacy and also provides useful evidence for the further development of attractive digital resources. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

25 pages, 1238 KB

Open AccessArticle

Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills

by Tommy Tanu Wijaya, Qingchun Yu, Yiming Cao, Yahan He and Frederick K. S. Leung

Behav. Sci. 2024, 14(11), 1008; https://doi.org/10.3390/bs14111008 - 30 Oct 2024

Cited by 55 | Viewed by 11493

Abstract

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their [...] Read more.

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

Review

Jump to: Research, Other

14 pages, 1141 KB

Open AccessReview

The Impact of AI on Learners’ Self-Efficacy: A Meta-Analysis

by Liling Ren, Jason M. Stephens and Kerry Lee

Behav. Sci. 2026, 16(1), 158; https://doi.org/10.3390/bs16010158 - 22 Jan 2026

Cited by 2 | Viewed by 6885

Abstract

With the rise of generative artificial intelligence, the application of AI in learning environments has received widespread attention. Although empirical studies have explored the effect of AI on self-efficacy, the results have not been consistent. This study conducted a meta-analysis on the results [...] Read more.

With the rise of generative artificial intelligence, the application of AI in learning environments has received widespread attention. Although empirical studies have explored the effect of AI on self-efficacy, the results have not been consistent. This study conducted a meta-analysis on the results from 23 empirical studies on the impact of AI use on self-efficacy. These studies were published between January 2005 and February 2025 and indexed in one or more of the three major educational research databases: Web of Science, Scopus, and ERIC. The results indicated that AI had a significant positive impact on self-efficacy in learning contexts (effect size of 0.758). Specifically, discipline (Q = 10.348, p < 0.05) and the specific role played by AI (Q = 3.991, p < 0.05) significantly moderated the effect of AI on self-efficacy. In our discussion, suggestions are provided for enhancing learner self-efficacy and improving the effectiveness of AI in the learning contexts. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

Other

Jump to: Research, Review

26 pages, 711 KB

Open AccessSystematic Review

A Systematic Review of Responses, Attitudes, and Utilization Behaviors on Generative AI for Teaching and Learning in Higher Education

by Fan Wu, Yang Dang and Manli Li

Behav. Sci. 2025, 15(4), 467; https://doi.org/10.3390/bs15040467 - 4 Apr 2025

Cited by 33 | Viewed by 7917

Abstract

The utilization of Generative AI (GenAI) in higher education classrooms has significantly increased in recent years. Studies show that GenAI holds promise in impacting the learning experiences of both students and teachers, offering personalized learning and assessment opportunities. This study conducts a systematic [...] Read more.

The utilization of Generative AI (GenAI) in higher education classrooms has significantly increased in recent years. Studies show that GenAI holds promise in impacting the learning experiences of both students and teachers, offering personalized learning and assessment opportunities. This study conducts a systematic review of the responses, attitudes, and behaviors related to the application of GenAI within higher education classrooms. To this end, we synthesized 99 papers published between 2020 and August 2024, focusing on the utilization of GenAI in higher education settings. The analysis addresses three key inquiries: responses, attitudes, and behaviors. This systematic review provides an updated understanding from psychological perspectives of GenAI’s role in the teaching and learning processes of higher education, with a particular emphasis on GenAI technologies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

► Show Figures

Figure 1

1 pages, 131 KB

Open AccessCorrection

Correction: Chan (2025). AI as the Therapist: Student Insights on the Challenges of Using Generative AI for School Mental Health Frameworks. Behavioral Sciences, 15(3), 287

by Cecilia Ka Yuk Chan

Behav. Sci. 2025, 15(3), 375; https://doi.org/10.3390/bs15030375 - 17 Mar 2025

Cited by 1 | Viewed by 2087

Abstract

In the original publication (Chan, 2025), “(Gaffney et al [...] Full article

(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)

Journal Menu

Journal Browser

Artificial Intelligence and Educational Psychology

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI