Educating Aspiring Teachers with AI by Strengthening Sustainable Pedagogical Competence in Changing Educational Landscapes

Abstract

This study examines the effectiveness of an eight-week AI training program aimed at enhancing teacher candidates’ pedagogical competence and AI literacy in rapidly changing and evolving educational environments. As the modern world continues to change and develop, the transformation of education, which is one of the most important elements of our lives, cannot be ignored. Accordingly, the integration of teacher candidates, who constitute key education stakeholders, into technological developments is very important in terms of both efficiency and sustainability. The “parallel–simultaneous design”, one of the mixed research methods in which quantitative and qualitative research methods are used together, was employed. In line with the stated purpose, the study started with a needs analysis conducted with 33 teacher candidates studying in different branches at the faculty of education. As a result of the needs analysis, knowledge gaps, digital skill levels and readiness for integration of artificial intelligence tools in future classrooms were determined. Its application to teacher candidates, instead of teachers in the profession, was determined by the needs analysis. The results indicate that it would be more beneficial to apply the education of the future to the teachers of the future and that they will find it easier to adapt to such training. Accordingly, a pre-test–post-test design was applied to observe how the participants changed, and an artificial intelligence literacy scale was also used. QDA Miner Lite was used for the analysis of the qualitative data, and SPSS 29.0 was used for the analysis of the quantitative data. During the eight-week training, Gamma programs were used for the presentation, Suno for audio, Midjourney for visuals and ChatGPT-4 for a descriptive search in order to provide better quality education to the participants. While practicing with these applications, the aim is to provide more up-to-date education that reveals problem-solving skills that include critical thinking exercises. According to the results, the teacher candidates who expressed that they were undecided or had insufficient knowledge reached a sufficient level in the post-test. In the light of these results, it can be stated that artificial-intelligence-oriented education is effective in developing sustainable pedagogical skills, digital literacy, readiness and professional self-confidence. The study also offers evidence-based recommendations for the design of future teacher training programs.

1. Introduction

It is undeniable that all aspects of our lives are impacted by the rapid development of technology around the world. This rapid change and development on a global scale, in short, digital transformation, defines the 21st century. It has redesigned our social and cultural structures [1]. It is not possible for education, which has been an intrinsic aspect of life since the existence of human beings and starts from the family and continues into social life, not to be affected by this. In fact, this transformation has had the most profound effect on the field of education. Many new technological trends are starting to emerge in education [2]. The effectiveness of artificial intelligence and big data analytics, machine learning and language automation, which form the foundations of artificial intelligence, cannot be ignored. Indeed, as a result of this change, the structure of learning–teaching processes has been fundamentally transformed and traditional pedagogical models must be re-evaluated [3]. This change should not be considered only as the structuring or diversification of technological tools; all stakeholders in education must be updated. In particular, it is important to consider that teacher roles, student expectations, instructional designs and evaluation processes may completely change and will require restructuring. It may be necessary to examine the technological integration of all this change and transformation in education, analyze it thoroughly and redesign it [4].

The digitalization of education systems has also changed the roles and expectations of all stakeholders. Although it is considered sufficient for today’s teachers to adapt to technology at a certain level, the expectation is that they have effectively adopted and adapted to pedagogical approaches integrated with technology. This expectation will begin to be expressed more profoundly in the coming years. For this reason, the concept of “sustainable pedagogical competence” has become the fundamental principle of modern educational research. It is necessary to analyze this concept thoroughly and ensure that educators become assimilated. This concept not only includes the ability to use existing digital tools but also the ability to predict what may be encountered in the future, to adapt, to be innovative, and to have a flexible and critical teaching approach [5]. In this context, the relationship between sustainable pedagogical competence and digital literacy is of critical importance, especially for future teachers.

Within the scope of sustainable education, pedagogical competence fully reveals the skills of teachers [6]. Teachers should be people who are not limited to their existing skills but who can adapt to changing social needs, technological transformations and pedagogical and environmental conditions, who adopt lifelong learning and constantly improve themselves. Although sustainability is generally discussed in the literature through environmental factors, this concept should also be evaluated pedagogically [7], because the integration of the teacher, who is one of the most important stakeholders in education, into the new concepts of life is very important. This study is expected to contribute by addressing the deficiency in the literature.

In sustainable education, the impact of AI literacy is undeniable [8]. Due to the development of technology, artificial intelligence systems are everywhere in human life. With the effect of this, critical evaluation can be defined as a multidimensional structure that includes the effects of ethical consequences and the conscious use and competencies of these technologies. In the current literature, artificial intelligence has been examined in many fields. There are also many studies on artificial intelligence literacy. However, in this study, artificial intelligence literacy and sustainable pedagogical competence were examined by blending. For this reason, it was looked at from a different perspective. With this definition, it is obvious that the stakeholders of education are faced with a pedagogical decision-making process. Teachers and teacher candidates reveal that there is a lack of a holistic theoretical model on how to relate practices and education in this process [9].

An examination of the literature reveals that teacher candidates’ awareness of technologically developed tools and their ability to use these tools are crucial for the sustainability of education quality. Artificial intelligence literacy directly affects educators’ ability to create technology-integrated learning environments and skills in their future classrooms, while also contributing to key education processes, such as critical thinking, analytical decision-making, and ethical evaluation [10].

Furthermore, the impact, place, and role of artificial intelligence in education are gradually expanding. Although it has negative impacts such as potential misuse by students and efforts to reach conclusions from research, it also has significant positive impacts. In particular, systems that analyze students’ academic performance, personalize learning paths specific to the student, provide fast feedback mechanisms in terms of interaction, offer automatic evaluation tools for results or processes and have the ability to produce content digitally and increase pedagogical efficiency all reduce the workload of educators. Efforts to continue classical learning by trying to remove technology, which is a modern reality, from students’ lives are not effective for the new generation, as this decreases their ability to focus [11]. It is thought that technological integration will make significant contributions in terms of adequacy, motivation and learning–teaching efficiency for the new generation, who live in a society in which life and consumption are faster paced. In particular, artificial-intelligence-based applications and content production tools, speech production systems, visual creation platforms, text-based smart assistants that enable quicker research, etc., will contribute both positively to the development processes of teacher candidates and engage the attention of students to a greater extent. However, in order to understand the positive aspects of these tools, as well as to use them more effectively and ethically, teacher candidates and teachers need to undergo structured training.

In this context, it is not sufficient for technology to be integrated into the training programs created for teacher candidates. In fact, technology should be made a part of the pedagogical process, with applied, two-sided participation, passive student activation and active participation that focuses on critical thinking [12]. Many studies in the literature have revealed the effects of application-based artificial intelligence training on teachers and students. Various researchers have shown that application-based artificial intelligence trainings increase teacher candidates’ professional self-efficacy levels, reduce their anxiety about technology and digital tools and, most importantly, positively affect their attitudes toward technology integration [13]. For this reason, it has become a strategic priority of education faculties for teacher candidates to gain active experience in technology-integrated learning environments before starting their profession.

The concepts of sustainability of education and the roles of future education in order to make the future healthier have assumed greater significance. Sustainable education is not just a concept that encompasses environmental awareness [14]; it is a holistic learning approach that supports the continuity and long-term continuity of life in economic, technological and social terms. With the integration of technology into education, the shape of many materials has changed and will change further [15]. In this regard, the harm caused to nature by consumption will decrease and the style of education for the new generation of people will increase. Recent research reveals that a sustainable education system is very valuable, and this value will increase in terms of nature, human and life sustainability [16]. It is seen that digital transformation, innovative pedagogical approaches and lifelong learning skills are the main components of this education style. From this point of view, it is clear that artificial-intelligence-based applications and technologies are important elements that will shape the future of sustainable education because artificial intelligence offers many advantages that strengthen sustainability in our lives, such as personalization of learning, accessibility, efficient use of resources, reduction of physical resource use and support of teaching processes with automation. Most importantly, it will be able to reduce education-based consumption by mobilizing and digitalizing education. Therefore, increasing the artificial intelligence literacy and digital pedagogical competencies of teacher candidates is vital for the continuity of sustainable education and the quality of the learning environments of the future. By adopting an approach that supports the vision of sustainable education, this study aims to contribute to the education of the future by training the teachers of the future such that they can become active, competent and adaptable professionals.

The primary motivation for conducting this study is the inadequacy identified in the artificial intelligence literacy levels of teacher candidates and the need to strengthen their sustainable pedagogical competencies. At the beginning of the study, the lack of knowledge, weak digital competence levels and limited ability to integrate technology into classrooms were revealed by both expert analyses and needs analyses, and the content of the eight-week training program designed in this context was structured and implemented accordingly. The results of this needs analysis are in agreement with many other studies in the literature [17]. Although the digital access of teacher candidates has increased, their competencies, effectiveness and integration into systematic education are insufficient.

The applications in the program developed for this study include the practical use of the most preferred modern artificial intelligence tools, such as for presentation designs (Gamma), voice generation (Suno), visual design (Midjourney) and text-based querying (ChatGPT). Both the correct use and the advantages and disadvantages of the applications are based throughout the training. The reason why these tools were chosen was to support the most frequently used material production skills such as visual, auditory, research and presentation skills that teacher candidates may need in their careers [18]. Additionally, the pedagogical dimension of the program was strengthened through critical thinking exercises and problem-solving activities, emphasizing that technology is not only a technical tool but also an element of pedagogical innovation.

The fact that the research was evaluated with the pre-tests and post-tests carried out under the same conditions provided the opportunity to quantitatively reveal the effects of the program on teacher candidates. In the qualitative part, the study was supported by needs and individual comments.

As a result, it can be stated the integration of sustainable pedagogical competence and AI literacy into teacher training processes is no longer seen as a choice but a necessity in rapidly changing educational landscapes. This study demonstrates the effectiveness of applied AI training programs for pre-service teachers, makes a significant contribution to the teacher education literature, and provides evidence-based recommendations for the development of technology-based teacher training models in higher education.

The Aim of the Study

In light of all this, it is important to recognize the necessity of AI-supported education in education for prospective teachers studying at the university level. The basis of this acceptance is that the speed of developing technology should not be ignored. For this reason, a sustainable education program based on artificial intelligence should be created. Not only artificial intelligence but also all technological integration should be completed. This study focused on the artificial intelligence part. The main purpose of this study is to develop an artificial-intelligence-based sustainable education program for teacher candidates studying at the university level and to evaluate its effectiveness. The research questions designed to achieve this general goal are as follows:

• What are the knowledge levels and opinions of teacher candidates regarding the education program based on artificial intelligence integration?
• To what extent did the digital literacy, readiness and knowledge levels of the teacher candidates participating in the training on sustainable educational practices of artificial intelligence change?
• What impact does participation in an artificial-intelligence-based teacher training program have on the professional self-confidence and pedagogical sustainability levels of teacher candidates?

2. Materials and Methods

In this part of the study, the model of the research, the scope of the research, sample group data collection tools, learning activities and lesson plans, and statistical methods used in data collection and analysis are explained. This study aimed to train teacher candidates with artificial intelligence by strengthening sustainable pedagogical competence in changing educational environments. As a result of the needs analysis, an 8-week training program was designed. With this training, teachers were comprehensively taught how to use four different applications, namely Gamma, Suno, Midjourney and ChatGPT, so they could apply them in their education. Project assignments were given for all these applications and these assignments were then evaluated. At the end of the training, certificates were given to the successful participants.

In this research, a mixed methods approach that integrates both quantitative and qualitative research techniques was applied to create a sustainable education program centered on artificial intelligence applications and to evaluate the effectiveness of the implementation and results of this program. One of the most important factors influencing the choice of the mixed methods model is the limitations of a single method and the elimination of the possibility of evaluating the data in a versatile and comprehensive way [19]. In this way, the findings were strengthened through the use of multiple data sources. It is critical for the researcher to choose the right model before starting their study in order to choose the right design [20].

The most appropriate mixed method approach for this study was determined to be the “parallel–simultaneous design” [21]. The most important reason for choosing this design is that qualitative and quantitative methods are carried out simultaneously. This model enables data to be collected at the same time, independent analyses to be performed separately and comprehensive results to be obtained [22]. Before planned training was started, both a pre-test and qualitative scale were applied and data were obtained. At the end of the research, a post-test and qualitative scale were then applied. The main advantage of this model is its capacity to produce more detailed and comprehensive results.

In the quantitative part of the research, it was preferred to use screening and experimental methods together. In the screening phase, the descriptive-relational survey model was implemented because it allows an event or situation to be described as it is without changing it while also revealing the different relationships between variables. In the experimental study, the focus was on evaluating the changes in the participants’ levels after the intervention had been applied [23]. The main purpose of experimental research is to reveal the effect of a certain change or development on the participants as a result of the procedures applied in line with the determined purposes. In this type of research, the researcher actively interacts with at least one of the groups examined. In the qualitative part of the research, the “case study design” was used. This approach allowed for an in-depth and longitudinal examination of the dynamics taking place in the application environment throughout the implementation process and the interpretation of the findings regarding the changes in the implementation process. In line with the detailed analysis required by this design, interviews and document reviews were conducted. In addition, the opinions of the teacher candidates were collected in the qualitative phase of the research and the obtained data were then evaluated. Documenting and analyzing the collected data is considered important in case studies as this provides an empirical resource.

This research was conducted in the fall semester of the 2024–2025 academic year. In the qualitative phase of the study, 33 participants selected via the purposive sampling method took part. This method was preferred because of its potential to obtain in-depth and information-rich data. This approach allowed for a thorough examination of key operational contexts. In addition, certain criteria were established to determine the study group and it was envisaged that the sample selected according to these criteria would cover all relevant dimensions allowing the objectives of the research to be achieved. In the needs analysis process, it was planned to provide access to previously published scientific resources on “artificial intelligence” and “sustainable education”. In this phase, a qualitative scale consisting of five questions was developed in order to evaluate the perspectives of the pre-service teachers on artificial intelligence applications and sustainable education in line with the opinions of experts in ten different fields. The main purpose of administering this scale was to determine the readiness levels of pre-service teachers for the research topic. In the evaluation process, the criterion sampling method was used where the main criterion was participants’ “having taken computer courses before”. Accordingly, a total of 33 teacher candidates studying at the university in the fall semester of the 2024–2025 academic year who met this criterion were identified and selected for participation in the research. These participants constituted the sample group of the research.

In the needs analysis phase, interviews were conducted with field experts to find answers to the question “What should be taught?”. Certain criteria were determined when selecting the experts, including that that they had to be teachers and academicians providing education in fields such as technology courses, education model courses, digital content development courses, instructional technologies, computer courses and technology education. In addition, the aim was to provide more comprehensive evaluation by including the opinions of academics from different faculties with similar experience and expertise.

In the quantitative part of the research, the participants who would be subjected to the experimental application were selected by the appropriate sampling method. The reason for choosing this method is that it provides convenience to the researcher in terms of time, resources and effort, because the participants must have a certain level so that it serves its purpose in a way that will be useful in this training. As a result, a total of 33 pre-service teachers studying in the fall semester of the 2024–2025 academic year constituted the experimental group. All of the field experts who contributed to the creation of the developed training program with their opinions and the teacher candidates who participated in the application lived in Cyprus and their demographic information is presented in

Table 1. Distribution of demographic information of participants.

			(f)	(%)
	Distribution of pre-service teachers by gender	Female	17	51.51
		Male	16	48.49
Experimental Working Group	Distribution of pre-service teachers by age	18–25	27	81.82
		26–35	6	18.18
		36–45	0	0.00
		45>	0	0.00
Field Experts Working Group	Distribution of experts by gender	Female	5	50.0
		Male	5	50.0
	Distribution of experts according to their experience in the profession	<5	2	20.0
		6–11	5	50.0
		12–17	2	20.0
		18>	1	10.0
	Distribution of experts according to their work on AI applications and sustainable education	I taught the same/similar course content	10	100.0
		I made a presentation at a conference	4	40.0
		I wrote a book/chapter	1	10.0
		I wrote a thesis	4	40.0
		I made a scientific publication	5	50.0
	Distribution of experts by title	Professor	5	50.0
		Assist. Prof. Dr.	3	30.0
		Instructor	2	20.0

.

2.1. Experimental Working Environment

In this study, the application was carried out as a face-to-face training program for 2 h a week for 8 weeks. The program was applied to teacher candidates studying at the Faculty of Education of the University of Kyrenia. Interaction between the researcher and the participants was provided. In this process, the boundaries were drawn correctly and notified to the participants with an informed consent form in order not to affect their education. In this way, instant feedback was provided throughout the learning process, and discussion and critical thinking parts could be conducted. Face-to-face interactions provided to the opportunity to participate in and observe the training and to ensure the effectiveness of the intervention.

2.2. Teaching Activities Program

The output of the teaching activities in this study was prepared by taking expert opinions into account. In the prepared program, after each application week, project assignments were given to the participants and they were subsequently evaluated. Successful participants were also given certificates. All 33 participants were successful. During the face-to-face training, the participants interacted with the researcher and received support in the project development process.

During the 8-week training period, the interventions given to the participants include tool-oriented activities. The reason for this is, it should be noted, it is within in a framework that aims to increase the competencies of teacher candidates at a sustainable education level. Although the applications used here seem to be at the forefront, the criteria for the preference, application and evaluation of those tools have been completely pedagogically supported and prepared. This aims for broader pedagogical impact and integration with AI tools. The main goal of the tools used to achieve the objectives is to integrate the participants into different basic pedagogical teaching contexts and to be able to transfer them. First, it is to train teacher candidates to have knowledge about artificial intelligence literacy and then to raise technologically competent new generations trained by them to ensure pedagogical sustainability. This is exactly the purpose of the developed program.

As shown in

Table 2. Subjects, outcomes, learning time and percentile.

Week	Objective/Content	Achievement	Tool/Platform	Duration	Percentile
Week 1	AI and Sustainable Education: Foundations & Future of Learning	Understand sustainable pedagogy, AI literacy, future classrooms, digital transformation dynamics	Introductory Seminar	2 h	5%
Week 2	Creating AI-Based Instructional Presentations	Ability to design effective and sustainable instructional presentations using AI structuring tools	Gamma	2 h	12.5%
Week 3	Developing AI-Based Audio Materials	Ability to generate narration, audio content and sound-based instructional materials	Suno	2 h	12.5%
Week 4	Generating AI-Based Visual Educational Materials	Ability to create visuals, digital posters, learning graphics and scenario-based images	Midjourney	2 h	12.5%
Week 5	AI-Driven Research, Content Development and Lesson Support	Ability to conduct descriptive searching, create content and design lesson materials	ChatGPT	2 h	12.5%
Week 6	Seminar Week: Pedagogical Applications of AI in Sustainable Education	Gain multi-perspective insights from experts in education, technology, pedagogy and sustainability	Expert Seminars	2 h	10%
Week 7	Application Competition Week (Project Development)	Develop original AI-supported micro-teaching projects using the tools learned (Gamma/Suno/Midjourney/ChatGPT)	Project Competition	4 h	20%
Week 8	General Evaluation & Reflection Week	Evaluate projects, measure learning outcomes, reflect on sustainable & AI-enhanced pedagogy	Evaluation Session	2 h	15%

, a detailed presentation was made by the researcher in the first week. For the next four weeks, the applications were applied and applied one by one. A seminar was given in the sixth week and applications were made by an expert trainer in the field. In the seventh week, a project development competition was held. In the last week, projects were evaluated and the participants’ learning outcomes were measured.

Also in the weeks (2-3-4-5) where each practice is taught, homework related to the practices is given in order to strengthen the evaluation. Homework has helped to determine the students who will be successful in the education process. Also in week 7, the competencies of the teacher candidates were tested with the project competition held during the week. During the last week, week 8, in the general evaluation and information part of the week, the whole process was reviewed with all participants.

2.3. Data Collection Tools

Data were collected face-to-face, but they were collected digitally through Google Forms.

2.3.1. Qualitative Data Collection Tools

Interview form questions were sent to the pre-service teachers online via “Google Forms” in order to collect qualitative data. Although the application was carried out online, the training was conducted in a classroom environment so that the researcher could make observations [24]. In line with the feedback received from the participants, analyses were made and deficiencies related to the training were identified. Considering these deficiencies, the training program was redesigned accordingly.

In this research, two different forms were used to collect qualitative data. In addition, QDA Miner Lite program was preferred as the analysis method. Descriptive content analysis was performed and the data were divided into variables and themes. Analysis was made according to these determined themes. The self-assessment form was applied first. Then, the expert trainer interview form was applied and the process evaluation was started. The need was revealed with the qualitative scale created. Later, this study was supported by the document analysis method. The study was conducted face-to-face from start to finish. Although face-to-face, the data was collected digitally. The aim here is to emphasize the importance of digital transformation. In addition, the Google Forms platform was preferred to be online in order to prevent data loss and to analyze the data more easily for more effective use.

2.3.2. Quantitative Data Collection Tools

In terms of the validity and reliability of education, the “Artificial Intelligence Literacy Scale” developed by Uğur Demir, Fatih Yılmaz, and Celalettin Çelebi, in 2023, was applied to determine the perception and knowledge levels of teacher candidates about artificial intelligence [25]. Permission was obtained from the relevant authors before application. In addition, detailed information was given to the participating teacher candidates and experts whose opinions were obtained about the “validity and reliability” of this scale. In addition, demographic data of the pre-service teachers were collected while applying this scale. Their artificial intelligence knowledge levels were subsequently determined within the scope of the 8-week training and the developed trainings were implemented within this framework. Within the scope of the training, detailed information was given about how the selected practices will be implemented, what the pedagogical expectation is, how they are designed and what their pedagogical contribution will be. At this level, it was tested with homework and in-class practices in order to determine the knowledge and development levels of the participants. In order to reveal the development, an 8-week process evaluation was put forward with pre-test and post-test applications. After the training was completed, the same scale was applied again under the same conditions. The same analyses were applied to these data, and the development of the process was tried to be evaluated. All these analyses were conducted in the SPSS 29.0 package program. The significance level of the statistical data was determined as 0.05.

2.3.3. Informed Consent Form

The researcher prepared an informed consent form to collect the demographic characteristics of the participants. This form was tailored to criteria such as age and gender, which may influence teacher candidates’ perceptions, attitudes and opinions toward AI applications. For the experts, specific criteria, such as years of professional experience, profession and title, were also added.

In order to eliminate possible pressure and anonymity problems on the basis of the voluntariness of the participants, an informed consent form was obtained before starting the study. With this form, all possible problems were mentioned and the consent of the participants was obtained. In the form, it was stated in detail that the purpose of the study, universe-sampling, data collection and analysis processes, the preference and face-to-face activity and effectiveness of the digital environment, what the criteria for the observation to be made in the classroom were and also that the participants could withdraw from the study at any time. In addition, the data analysis was fully anonymized while collecting and analyzing, and the identities and/or digital identities of all participants are protected. This process was completely voluntary by the participants and aimed to ensure data confidentiality. Necessary actions were taken to prevent possible problems from arising. The participants were informed that the data would only be used for research and that the data would only be kept by the researcher.

2.3.4. Artificial Intelligence Literacy Scale

The “Artificial Intelligence Literacy Scale”, developed to determine the artificial intelligence knowledge levels of teacher candidates and used after obtaining the necessary permissions, was prepared by Uğur Demir, Fatih Yılmaz and Celalettin Çelebi in 2023 [26]. The Artificial Intelligence Literacy Scale is a scale designed to measure the cognitive knowledge levels of teacher candidates about artificial intelligence. The scale consists of 12 questions in total and 4 factors covering these questions. Thus, the knowledge and perceptions of the participants in different dimensions can be evaluated. As a result of the analysis, the general internal consistency of the scale was found to have a Cronbach’s alpha coefficient = 0.85, and it was determined that the scale was a reliable measurement tool. The scale has a Likert-type structure with 7 degrees. Participants respond by selecting one of the following options for each statement: “Strongly Disagree (1)”, “Disagree (2)”, “Partially Disagree (3)”, “Undecided (4)”, “Partially Agree (5)”, “Agree (6)”, “Strongly Agree (7)”. This structure allows for a more detailed and nuanced measurement of participants’ thoughts and perception levels. This structure of the scale provides suitable data for quantitative analysis and allows for a comprehensive evaluation of the artificial intelligence literacy levels of teacher candidates.

2.4. Data Analysis and Interpretation

2.4.1. Analysis of Quantitative Data

In the analysis process of the data collected with the Artificial Intelligence Literacy Scale, the SPSS 29.0 program and a significance value of 0.05 were used. First, normality tests were applied to determine the conformity of the data to the normal distribution. As a result of the data obtained here, graphical controls were made to examine the normality status of the data. Then, skewness and kurtosis values were calculated. All these processes were conducted by researchers. As a result of the analysis, the normality distribution was determined, and it was revealed that parametric tests could be applied according to the results obtained. In the literature, it is stated that when a normal distribution is not achieved, the Type I error rate may increase, and this may adversely affect the result [26]. Therefore, it is important to verify that the data are normally distributed so that parametric methods can be used safely. According to the findings, the t-test was applied. This test was preferred because it is particularly suitable for comparing the means of two measures for the same individuals. The analyzed data are presented in detail in tables in the findings section of the study. This methodological approach enables a systematic and reliable evaluation of both pre- and post-training development processes. Laboratory environment was chosen while carrying out activities in the relevant applications. In addition, the prepared projects and applications were made through the personal e-mails of the users. While doing this, data privacy is taken as a basis. Only the projects of the participants were evaluated as presentations during the evaluation process. In addition, the applications were asked to be taken “screenshots” on the computers of the participants and filed and delivered to the researcher. While doing all these, the aim was to ensure that the data privacy of the programs and the data privacy and ethics of the participants were not violated.

2.4.2. Document Analysis

In the research conducted within the scope of document analysis, the descriptive analysis approach was preferred. While choosing this approach, both the literature was examined and opinions were taken from experts. The reason why this method is preferred is that it reveals certain patterns and themes. The data collected during the analysis process were systematically recorded and regularly divided into themes and variables by the researcher. Thus, the relationships and commonalities among different data types are clearly revealed. In addition, the observations of the researchers in the training process and the processes of the applications also contributed.

2.4.3. Analysis of Qualitative Data

The data of the study were collected online for compliance in terms of technological integration, but the data were collected face-to-face. Descriptive content analysis was applied in the analysis process. It was conducted using the QDA Miner Lite program with content analysis and continuous comparison techniques. In this way, the perceptions, attitudes and mindsets of the teacher candidates were systematically examined. By observing the teacher candidates, the researchers also examined their effects on determining the variables and themes. In addition, the development processes of teacher candidates were examined both qualitatively and quantitatively from the beginning to the end. The aim here is to reveal the development and interaction patterns of the participants regarding their processes.

2.4.4. Validity and Reliability Study in Qualitative Analysis

Careful methods were applied in the analysis process to ensure that the qualitative data were reliable and valid. In the research, internal validity, external validity, reliability and objectivity criteria were taken into consideration. The consistency of the data was supported by expert opinions and participant feedback. In this way, the accuracy of the findings and their contribution to the literature were strengthened. The accuracy and contribution to the literature here are supported by

Table 3. Evaluation process within the scope of reliability and validity.

Factor	Process
Internal Validity	The data started with the needs analysis and were completed with the post-test. For this reason, a permanent effect has been achieved. While collecting the data, the researchers and participants interacted one on one. The findings were explained clearly and transparently. The collected data were analyzed at the theme level. Accurate and complete coverage has been provided according to the defined themes.
External Validity	The findings are provided with clear and understandable quotations in terms of transmissibility. Practitioner information is given in detail. Participants were selected according to the case by the purposive sampling method.
Reliability	The researchers carried out the analyses individually. Different places, different times were preferred. All findings were then collated in a common way, reviewed by an impartial expert and finalized. Teacher candidates participating in the study were informed in detail with an informed consent form.
Neutrality	In the processes of analysis and interpretation of the data, it is important for researchers to act objectively and impartially. There should be no bias or data distortion by working on anonymized data. These processes were verified by an impartial observer. The results and evaluations were evaluated by an impartial expert and finalized. The researcher has clearly declared his/her responsibilities regarding the process in the informed consent form and also by the researchers.

.

3. Results

3.1. For the First Research Question

A needs analysis was conducted in line with the first research question of this study, “What are the knowledge levels and opinions of teacher candidates regarding the education program based on artificial intelligence integration?”. While preparing the questions for the needs analysis, expert opinions were obtained and the qualitative scale of the study was created by examining the findings obtained from them. With this qualitative scale, the aim was to reveal the views of the participants taking part in the study toward artificial intelligence. In addition, in the continuation of these opinions, the aim was to determine the digital literacy, readiness and knowledge levels of the participants.

As can be seen in

Table 4. Qualitative scale findings for the needs analysis.

Factor	Codes	n	f
1. Do you know about artificial intelligence?	1 = None, 2 = Basic, 3 = Medium/Detailed	None: 5, Basic: 15, Medium/Detailed: 13	15%, 45%, 40%
2. In which areas can AI be used?	1 = Education, 2 = Health, 3 = Finance, 4 = Daily life/Other	Education: 18, Health: 12, Finance: 7, Daily life/Other: 10	55%, 36%, 21%, 30%
3. Will artificial intelligence contribute to our lives?	1 = Negative, 2 = Unstable, 3 = Positive	Negative: 4, Undecided: 6, Positive: 23	12%, 18%, 70%
4. Will you use AI in your lessons?	1 = Yes, 2 = No	Yes: 22, No: 11	67%, 33%
5. Can AI pose a problem for creativity?	1 = No, 2 = Partially, 3 = Yes	No: 6, Partially: 14, Yes: 13	18%, 42%, 40%

, the knowledge levels of most of the participants were insufficient or moderate. In the in-class evaluation conducted with the participants who said they were at an intermediate level, it was determined that they only knew about the “ChatGPT” application. In fact, almost all of the participants were found to have insufficient or unintellectual knowledge in terms of integrating AI applications into sustainable education. The results of the detailed data collected regarding all these statements are presented in detail in Table 4.

3.2. For the Second Research Question

With regard to the second research question of this study, “To what extent did the digital literacy, readiness and knowledge levels of the participants taking part in the training of teacher candidates on sustainable educational practices of artificial intelligence?” A pre-test was applied with a quantitative scale at the beginning of the 8-week training program and then at the end of the research, the post-test was applied and the development of the participants was examined. The relevant findings are shown in

Table 5. Pre-test–post-test analysis.

	N	$\overline{\mathbf{X}}$	Ss	t	p
Pre-Test	33	2.44	0.42	10.423	0.000
Post-Test	33	3.97	0.34

N: participants, $\overline{\mathbf{X}}$: means, Ss: std. deviation: t: t-test significance level; p; statistical significance level.

and

Table 6. Wilcoxon test results of the sub-dimensions of the AI perception levels of the experimental group, pre-test and post-test t-test analysis.

Test	Dimension	With	p
Pre-Test–Post-Test	Knowledge	3.52	0.00 *
Pre-Test–Post-Test	Digital Skills	−2.94	0.432
Pre-Test–Post-Test	Readiness	−4.31	0.00 *

(p < 0.05). There is a significant difference. (p > 0.05). There is no significant difference. * This symbol was used to indicate significant differences.

.

While evaluating the data, statistical tests showing normal distribution and variance homogeneity or to examine the status of their assumptions were chosen. For normally distributed data, it was accepted that they met the homogeneity assumptions and t-test was applied. On the contrary, Wilcoxon tests were applied for data that did not provide it.

The average scores for the artificial intelligence literacy scale that the study participants achieved in the post-test (X = 3.97 − SD = 0.34) are higher than in the pre-test (X = 2.44 − SD = 0.42). According to the interval scoring presented in Table 5, those who answered “I am undecided” before participating in the training reached the level of “I agree” at the end of the training. It was, therefore, determined that there was a significant difference between the pre-test and post-test mean scores regarding the level of artificial intelligence literacy (p > 0.05). According to the t-test results, a significant difference was found in favor of the post-test (t(75) = 10.423; p < 0.05). In this case it was determined that the participants experienced positive development. An examination of the results shown in Table 6 reveals the areas in which this development occurred.

For the assumption of a normal distribution as the opposite of the t-test and do not provide it homogeneously, this test was applied to reveal the relationship or differences between the medians of the two groups.

When considered separately in the three dimensions, a significant difference was observed between the pre-test and post-test scores in the knowledge dimension. This result revealed that the training process added a positive significant increase to the participants. There was no statistically significant difference in digital skills. In other words, it did not create a significant change in the digital skills of the participants during or at the end of the training. In the readiness dimension, it was revealed that it made a significant difference and contributed positively to the participants in the training process. The main reason why the Wilcoxon test is preferred is that the digital skills dimension does not change significantly and does not show a normal distribution.

As shown in Table 6, a positive improvement in the level of knowledge and literacy was observed (p < 0.05). However, it was determined that there was no change in digital skills readiness levels (p > 0.05)

3.3. Third Research Question

The third research question of this study was “What effect does participation in an artificial-intelligence-based teacher training program have on the professional self-confidence and pedagogical sustainability levels of teacher candidates?” According to the findings, the participants benefited significantly from training, stating that they could reconcile sustainable education and artificial intelligence. They also mentioned that the eight-week training program was very productive. They stated that examining one application in detail during each week of training and the effectiveness of the application was very useful both in terms of integration into the application and the guidance of the researcher. They also reported that they found it very useful to test themselves in terms of their professional self-confidence and competence with the competition held in the seventh week.

As shown in

Table 7. Course outcomes.

Course	Degree	(%)
Course 1: Introduction and AI in Education	Good	12.61
Course 2: AI Tools for Content Design (Gamma)	Very good	8.31
Course 3: AI for Creative Teaching Materials (Suno)	Very good	9.17
Course 4: AI-Supported Student Interaction (MidJourney)	Very good	8.26
Course 5: AI in Assessment and Feedback (ChatGPT)	Very good	8.34
Course 6: Pedagogical Strategies with AI (Seminar)	Good	4.01
Course 7: AI and Sustainable Education Practices (Project)	Very good	8.12
Course 8: Project Presentation and Evaluation	Very good	8.26

, the majority of courses were rated as “Very good,” indicating a high level of student satisfaction with AI-integrated educational activities. This suggests that the inclusion of AI tools such as Gamma, Suno, MidJourney, and ChatGPT positively contributed to learning outcomes.

3.4. Opinions of Pre-Service Teachers on the Sustainable Education Program Based on Artificial Intelligence Applications

When the outputs obtained by the pre-service teachers based on artificial intelligence applications and their answers to the question “Would you define yourself as a new generation teacher?” are examined, it is seen that the majority (n = 27) expressed the belief they have the characteristics of a new generation teacher. When the answers given by the teacher candidates to the question “When you consider the sub-dimensions of sustainable education based on Artificial Intelligence applications, which sub-dimension did you find most appropriate and sufficient?” are examined, it can be understood that they sub-dimension in which they were most was the “readiness” sub-dimension.

“Did the sustainable education and training program based on Artificial Intelligence applications ensure your active participation in the learning–teaching processes of the course? Why?” and cited the effectiveness of active participation and scenario-based learning as the reason for this.

Participants responded “yes” to the question “Do you think that the sustainable education course based on Artificial Intelligence applications should be taken as a normal course by all teacher candidates in the future? Why?” Reasons for this answer included the necessity of adopting the right behaviors in digital environments and the need to remain up-to-date with the digital age (See

Table 8. Teacher candidates’ views on the sustainable education program based on artificial intelligence applications.

Dimension	Category		(f).	(%)
Based on the achievements of teacher candidates in the sustainable education course supported by AI applications, participants were asked whether they define themselves as new-generation teachers. The findings related to this question are presented below.	Thinking that they have the characteristics of a new generation teacher		32	82.05
	Thinking that they do not have the characteristics of a new generation teacher		7	17.95
Findings regarding the sub-dimension considered most appropriate and sufficient within the AI-based sustainable education framework for pre-service teachers are presented below.	Technology		12	30.77
	Digitalization		5	12.82
	Social		1	02.56
	Lifestyle		5	12.82
	All		16	41.03
Participants were asked about their views on the scenario-based teaching approach/activity implemented in the sustainable education course based on AI applications for pre-service teachers. The findings related to this question are presented below.	Effective learning		9	23.07
	Learning by experience		5	12.82
	Rapid learning		5	12.82
	Active participation		4	10.25
	Persistent learning		3	07.07
	Thinking skills		3	07.07
	Collaborative learning environment		1	02.05
	Effective learning		9	23.07
Did the learning–teaching process of the teacher candidates’ sustainable education course curriculum based on AI applications enable you to actively participate in the lesson? Findings on the question	Answer	Reason?	(f).	(%)
	Yes	Active participation	13	33.33
	Yes	Scenario-based learning	8	20.51
	Yes	Self-assessment	8	20.51
	No	-	0	0
Findings regarding participants’ opinions on offering the AI-based sustainable education course as a compulsory or regular course for all pre-service teachers are presented below.	Answer	Reason?	(f).	(%)
	Yes	Correct behavior in digital environments	15	80.00
	Yes	Adapting to the digital age	13	33.33
	Yes	Forward-looking	8	20.51
	Yes	Occupation, job skills	3	07.07
	No	-	00	00.00

).

4. Discussion

The aim of this research was to create a sustainable eight-week training program by using artificial intelligence applications and then to evaluate the effectiveness of this program. The appropriate requirements for an artificial-intelligence-centered sustainable education program were firstly determined. Then, a class studying in higher education was created and an eight-week experimental study was subsequently carried out. Various methods including pre-test–post-test and participant behavior scales were used to evaluate the effectiveness of the program. The findings revealed the effectiveness of the sustainable education and training program that includes artificial intelligence applications for pre-service teachers studying at the undergraduate level.

This study also examines the effects of artificial intelligence integration on new-generation teacher education; in fact, it makes an important and original contribution to the literature, because while doing this examination, it has also adopted the principles and effects of sustainability in order to pass it on to future generations. In particular, artificial intelligence reveals its role in increasing professional self-confidence and sustainability perception while prioritizing literacy. In this respect, it differs from the studies in the literature. If the study is evaluated from another perspective, it is an evaluation of pedagogical processes in a sustainable dimension. Addressing all these, it proposes the training of new teachers in an integrative and inclusive modern style. Obviously, it offers important inferences about the new generation of teacher profiles in terms of pedagogical approaches and sustainability.

4.1. Discussion on Qualitative Data

The qualitative findings of this research reveal that the knowledge, attitude and readiness levels of teacher candidates toward the concept of artificial-intelligence-based sustainable education were insufficient before the training. The majority of the participants stated that they only had introductory knowledge about artificial intelligence, while those who stated that they had intermediate knowledge only knew about ChatGPT in general during the implementation process. This reveals that although the participants’ access to technology was at a high level, their ability to use artificial intelligence tools for pedagogical purposes was not yet sufficiently developed. For example, in a study by Zhai et al. (2024), it was found that pre-service teachers’ awareness of artificial intelligence was low, and they were particularly hesitant to use the applications for a pedagogical purpose in the course [27].

The feedback obtained during the eight-week training period showed that the teacher candidates were especially motivated in terms of readiness and self-improvement and experienced an observable improvement. In a similar study, Holmes et al. (2022) found that AI applications supported participants’ interaction, problem-solving and critical thinking skills [28]. It has been frequently observed that applied activities, project-based learning and researcher guidance have a positive effect on teacher candidates. The majority of the pre-service teachers stated that the course in which artificial intelligence applications were used made them active learners, and that scenario-based activities in particularly made the knowledge more permanent. The fact that teacher candidates find scenario-based learning efficient reveals that artificial-intelligence-based teaching requires not only technical use but also pedagogical design. Similarly, Holmes et al. (2022) showed that AI applications support high levels of interaction, problem-solving and critical thinking skills in pre-service teachers [28].

In addition, the majority of teacher candidates defined themselves as “new generation teachers”, thus showing that the education process has changed in terms of the perception of professional identity. Participants stated that they realized that AI tools are not only technical tools but also a powerful support for pedagogical design and creative content production. The positive attitude, particularly toward tools like Gamma, Suno, Midjourney, and ChatGPT, reveals a strengthening of professional self-confidence. This result is in line with the findings of Falloon (2020), whose study showed that artificial-intelligence-based trainings significantly increase the digital pedagogical competencies of teacher candidates [29].

When the qualitative findings are evaluated, it can be said that artificial intelligence tools positively affect the capacity of teacher candidates to adapt to the digital transformation process, encourage classroom practices and significantly improve their awareness of sustainable pedagogical understanding.

4.2. Discussion on Quantitative Data

The quantitative findings showed that the eight-week AI-based training had a significant impact on the pre-service teachers’ knowledge, AI literacy, and readiness. An examination of the pre-test-post-test results revealed that the artificial intelligence perceptions, which were initially considered “undecided” increase to the level of agreement at the end of the program. This finding is in line with a study by Sweeney (2023), which showed that AI-supported teacher training increased the cognitive awareness and knowledge levels of teacher candidates even in the short term [30].

It is noteworthy that no significant change was observed in the digital skills sub-dimension in this research. This suggests that the technical skills of the teacher candidates may have already been at a certain level before the training or that the duration of the program may not have been sufficient for technical skill development. This finding is in line with a study by Yadav et al. (2016), who stated that short-term artificial intelligence training provides limited technical skills but a high level of improvement in cognitive awareness [31].

A significant increase in the level of readiness indicates that the pre-service teachers’ motivation to use these technologies in the classroom environment in the future increased. This result is similar to a study by Miao & Holmes (2021), which showed that AI integration improves professional confidence and innovativeness in teacher candidates [32].

As a result, the quantitative findings reveal that this program significantly improves teacher candidates’ professional competencies, artificial intelligence awareness, and adaptation to sustainable pedagogical approaches. Accordingly, it is of significant importance that artificial-intelligence-based sustainable educational content is systematically included in teacher training programs.

5. Conclusions

In this research, an eight-week training program was designed for teacher candidates studying in higher education. The effectiveness of the weekly artificial-intelligence-based sustainable training program was determined by quantitative and qualitative data. The findings show that the study participants were able to improve their artificial intelligence literacy. It had a meaningful and positive impact on their perception of sustainability and professional self-confidence. In particular, there was a significant difference between the pre- and post-test values, as they were significantly higher after the training program, reaching the level of “I agree” compared to the initial “Undecided” level. This result is supported by the qualitative data, as the teacher candidates took an active role in the education process, practiced in a pedagogical context and found themselves in the new-generation teacher profile. It was determined that they felt closer to what they required. Participants especially scenario-based learning, from the processes of producing applied content and exploring the interdisciplinary functions of artificial intelligence tools. It has been seen that they take power. The program made the change in teacher roles visible due to digital transformation, and it has been revealed that the concept of sustainable pedagogy can be reinterpreted by supporting artificial intelligence. He put it. In light of these results, it can be said that the integration of artificial intelligence into teacher training programs has now become a necessity rather than a choice. As a result of the rapid change in educational environments, teacher candidates must not only recognize technology but also understand how to use this technology for pedagogical purposes correctly, consciously and sustainably. It was revealed that the practices increased the teacher candidates’ awareness, motivation and readiness. it.

In the light of all these results, providing such trainings through distance education as the requirements of sustainable education will contribute to human life with advantages such as protecting nature, reducing fuel consumption, and decreasing electricity and building costs. In addition, it will reduce as the need to use materials such as pencils and paper, which are the classical education materials.

The increase in the artificial intelligence literacy levels of the teacher candidates was discussed and interpreted in relation to their active participation in scenario-based learning processes and also their experiences in applications that can contribute to the pedagogical level. It was an unexpected result that there was no increase in digital skill levels. It is thought that the fact that the participants are in Generation Z led to this result. However, in light of all the data obtained, a significant contribution to reducing the use of materials in terms of sustainability, saving resources and realizing environmental impacts has been made. In this way, it has provided an effective learning experience in education in terms of both pedagogical and sustainability in line with the desired goal.

It is recommended that such trainings be given not only to teacher candidates, but also to all education stakeholders. In addition, it is suggested that not only artificial intelligence technologies be integrated into sustainable education but all other technological trends should be considered. Applying this training to different age categories, in different locations and at different education levels with larger samples, can also make a significant difference.

These and similar studies, which are examples of technological integrations carried out within the scope of future and sustainable education, have an important place in the literature. In the light of scientific studies, it is recommended that new education models and new-generation teacher candidates are updated accordingly.

It is recommended that education stakeholders, education practitioners and politicians responsible for providing education focus on these issues.

6. Limitations

In this section, the limitations encountered in the research are discussed. It is mentioned in which aspects the research can be improved and in which aspects it is lacking. This study, which had 33 participants, could be carried out with a higher number of participants. The reason why this study is limited to 33 is that teacher candidates studying at the University of Kyrenia Faculty of Education were selected, and the criterion was that “having taken computer courses before” was obligatory. In addition, the training process was preferred for 16 h, 2 h for 8 weeks. With a longer training period, more applications can be tested and studies with higher academic inclusiveness can be carried out. The use of only artificial intelligence among technological trends can also be considered as a limitation. Other technological trends can be applied in similar studies. In addition, arrangements can be made according to the demographic variables of each country. If the programs are organized in a local context, their effectiveness can be greater. The study has been applied at a higher educational level, and it is thought that significant contributions can be made to both the literature and education in different fields by applying it at all levels of education.