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Generative AI in Educational Psychology Research

Generative Artificial Intelligence (GenAI) has emerged as a transformative tool in recent years, with applications spanning education, healthcare, and beyond. In educational psychology, GenAI offers innovative tools and approaches to understand how learners think, feel, and behave. Beyond its general applications in education, recent research highlights specific ways GenAI is being harnessed to advance our understanding of cognitive, emotional, and social aspects of learning.

Applications in Educational Psychology Research

While Generative AI was initially used in the domains writing and analysis, a wave of innovation has made it possible to integrate GenAI directly into experimental tasks and procedures, in order to study how participants perceive and interact with it.

Generative Artificial Intelligence (GenAI) is proving to be a versatile and powerful tool in educational psychology research, offering numerous applications. Here's how GenAI is being applied:

  • Enhancement of Problem-Solving Skills: AI-powered chatbots, such as ChatGPT, provide step-by-step solutions and targeted guidance, encouraging students to engage in critical thinking and apply problem-solving strategies (Miraglia, 2024).

  • Metacognition and Cognitive Processes: Metacognitive prompts generated by AI can enhance critical thinking during GenAI-based searches by encouraging learners to pause, reflect, and evaluate information (Singh et al., 2025).

  • Personalized Learning: The integration of GenAI can facilitate personalized instruction tailored to individual students' needs, enhancing the learning experience and accommodating diverse learning styles (Miraglia, 2024).

  • Analyzing Language: GenAI's advanced natural language processing capabilities allow for deeper analysis of student responses, providing insights into their motivation, frustration, engagement, and other emotional states (Întorsureanu et al., 2025).

  • Emotional Expressions: GenAI has been shown to enhance motivation, reduce anxiety, and foster an emotionally supportive environment. Tools providing real-time emotional feedback can also help in developing social-emotional learning competencies like self-awareness and empathy (Kohnke & Moorhouse, 2025; Henriksen et al., 2025).

  • Cognitive Scaffolding: Cognitive scaffolding refers to the support given to learners that helps them understand different concepts and that AI technologies can facilitate learning by providing tailored assistance based on a student's current level of understanding (Miraglia, 2024).

  • Improved Engagement and Interactivity: By simulating human-like conversations and interactions, generative AI can enhance student engagement and motivation to learn (Miraglia, 2024).

  • Development of 21st-Century Skills: Generative AI applications support the cultivation of essential skills such as creativity, teamwork, and communication, essential for thriving in a digitalized world (Miraglia, 2024).

  • Assistance during the Learning Process: AI chatbots serve as supportive study companions, providing explanations and clarifications across various subjects, which aids students in better understanding and contextualizing instructional material (Miraglia, 2024).

  • Empowering Educators: Gen AI technologies can simplify administrative tasks for educators such as scheduling, grading, and information dissemination. This allows them to focus more on teaching and student interaction (Miraglia, 2024).

Case Studies from Research Incorporating Generative AI in Experimental Tasks

Generative AI is gaining attention in educational psychology for its wide range of possible uses in research and practice. Here are some examples from research that show how generative AI is being used in educational psychology:

Generative Learning through AI-Based Tutoring

In a study by Makransky et al. (2025), generative AI was used through a special chatbot called ChatTutor. It prompted students to explain concepts and teach the AI, specifically leveraging principles from generative learning theory, indicating its grounding in educational theories. Students interacted with the ChatTutor system, which incorporated timely scaffolding and feedback calibrated to their understanding. This design facilitated active participation and aimed to create meaningful, student-centered learning interactions. Findings showed that using ChatTutor improved students' long-term retention of conceptual knowledge (Makransky et al., 2025).

Psychological Literacy through AI-Integrated Assessments

In a study (Richmond & Nicholls, 2024) generative AI (ChatGPT) was utilized in a three-phase assessment to enhance psychological literacy among undergraduate psychology students. In Phase 1 (AI-Assisted Draft Generation and Critique), students were required to use a generative AI tool to generate a draft media release. The students then critiqued this AI-generated content using a marking rubric, identifying strengths, weaknesses, and priorities for revision, and assigning grades based on the rubric. In Phase 2 (AI Critique), students revised the text produced by ChatGPT, incorporating their critique and documenting changes using tracked changes, before submitting their revision for instructor feedback. In Phase 3 (Revision and Video Production), students integrated feedback from their instructor, incorporated graphics, and made their final video submission. This method aimed to determine if early engagement with AI outputs would improve the quality of student work, and the results indicated significant performance benefits compared to traditional peer review processes.

Tailoring Educational Content to Student Interests Using GenAI

Tasdelen & Bodemer (2025) in their study, employed generative AI to create personalized educational content tailored to the individual interests of students. The study aimed to assess the effects of the AI generated materials on intrinsic motivation, interest, and learning performance. Generative AI created learning materials that aligned specifically with the interests identified for each student. Different tasks were designed using the generative AI and presented to students during the study. These tasks were designed to be context-personalized, adapting to the unique interests of the participants. The research involved a self-developed web application that facilitated access to the AI-generated content. Students participated in the study via this platform during regular math classes. The generative AI system was capable of dynamically generating educational materials based on the interests reported by students. This real-time capability ensured that the educational tasks remained relevant and engaging. Overall, the study shows that generative AI played a crucial role in creating better learning materials and thereby enhancing their learning engagement, motivation, interest and overall outcomes.

Scientific Text Comprehension via AI-Driven Mentorship

Generative AI was utilized through the OwlMentor platform in a study by Thüs et al. (2024), which is designed to assist students in comprehending scientific texts. The key applications of generative AI in this study included: Automatic question generation, AI-Powered dialogue where students could interact in a chat-like interface and discuss or ask questions, quiz creation with custom quiz generation and feedback. By using the AI's capabilities, the platform could tailor responses and prompts to individual students' inquiries, enhancing their learning journey through context-specific interactions. Users exhibited a notable improvement in their overall performance from pre-tests to post-tests, suggesting that the use of the platform positively impacted their learning outcomes across various topics.

Visual Learning and Critical Engagement through AI Image Generation

In a study (Berg et al., 2024), generative AI was utilized to facilitate student learning and enhance engagement. Students used Midjourney to create images based on prompts they developed. The generated images helped students move from a superficial understanding to a deeper, more critical comprehension of the topics discussed. They found that associating images with texts made information easier to memorize and understand. The use of AI-generated images served as a basis for classroom discussions. Students analyzed the images in relation to their prompts, discussing what aspects were accurate or misleading, thus enhancing critical thinking about the content. The study highlighted the motivating factor of students creating their own AI images, essentially a form of active learning, which they found more enjoyable than passively viewing existing images. Teachers suggested that the prompts used by students could serve as a basis for assessment, helping educators gauge students' understanding through the images they generated. The main finding of the study is that AI image generation tools, such as Midjourney, can positively impact student learning by making it more student-centered, interactive, fun, and engaging.

Conclusion

Generative AI is revolutionizing educational psychology by offering innovative solutions that support personalized learning, enhance engagement, and fosters critical thinking. As this field evolves, continued exploration will help discover even more meaningful ways to integrate AI into educational psychology research and practice. However, it is equally important to understand the ethical concerns around data privacy, bias, and the responsible use of AI in learning environments.

References

Berg, C., Omsén, L., Hansson, H., & Mozelius, P. (2024). Students' AI-generated images: Impact on motivation, learning and, satisfaction. International Conference on AI Research, 4(1), 500–506.

Henriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025). Social-emotional learning and generative AI: A critical literature review and framework for teacher education. Journal of Teacher Education, 76(3), 312–328.

Întorsureanu, I., Oprea, S.-V., Bâra, A., & Vespan, D. (2025). Generative AI in education: Perspectives through an academic lens. Electronics, 14(5), 1053.

Kohnke, L., & Moorhouse, B. L. (2025). Enhancing the emotional aspects of language education through generative artificial intelligence (GenAI): A qualitative investigation. Computers in Human Behavior, 167, 108600.

Makransky, G., Shiwalia, B. M., Herlau, T., & Blurton, S. (2025). Beyond the “wow” factor: Using generative AI for increasing generative sense-making. Educational Psychology Review, 37(3).

Miraglia, L. (2024). The promise of generative artificial intelligence. Psychological implications in educational contexts. Rivista di Scienze dell'Educazione, 62(1).

Richmond, J. L., & Nicholls, K. (2024). Using generative AI to promote psychological, feedback, and artificial intelligence literacies in undergraduate psychology. Teaching of Psychology, 52(3), 291–297.

Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: A systematic review and applications. Multimedia Tools and Applications, 84(21), 23661–23700.

Singh, A., Guan, Z., & Rieh, S. Y. (2025). Enhancing critical thinking in generative AI search with metacognitive prompts. arXiv preprint arXiv:2505.24014.

Tasdelen, O., & Bodemer, D. (2025). Generative AI in the classroom: Effects of context-personalized learning material and tasks on motivation and performance. International Journal of Artificial Intelligence in Education.

Thüs, D., Malone, S., & Brünken, R. (2024). Exploring generative AI in higher education: A RAG system to enhance student engagement with scientific literature. Frontiers in Psychology, 15.