Proceedings of the International scientific and practical conference ―Synergy of Modern Science and Education‖ (June 12-14, 2026) / Publisher website: www.naukainfo.com. – New York, USA, 2026. - 145 p.

85 perform tasks effectively under familiar conditions, capability refers to the capacity to apply acquired knowledge and skills in addressing novel, complex, and unpredictable professional challenges. The author regards the development of such capability as one of the key outcomes of heutagogical learning. For future teachers, this implies readiness to adapt to the dynamic changes of the educational environment, integrate innovative technologies into professional practice, and respond effectively to the contemporary challenges of teaching and learning. Artificial Intelligence as a Means of Supporting Autonomous Learning. T. Newfield considers artificial intelligence to be an important instrument for implementing the heutagogical approach, emphasizing that its effectiveness depends on its pedagogically appropriate and ethically responsible use. According to the author, AI should not control the learning process but rather support learner autonomy through personalized feedback, adaptive learning pathways, and opportunities for reflection. AI is particularly effective in fostering learner agency when its recommendations are transparent and do not restrict learners’ freedom of choice [5]. The Transformation of the Teacher’s Role in AI-Oriented Education. The author also argues that the integration of heutagogy and artificial intelligence technologies transforms the professional role of the teacher. Rather than serving primarily as a transmitter of knowledge, the educator increasingly assumes the roles of learning environment designer, facilitator, and mentor, guiding students in identifying individual learning goals, analyzing their own progress, and critically evaluating the outcomes of their interactions with intelligent digital systems [5]. The concept of heutagogy proposed by L. M. Blaschke and S. Hase has made a significant contribution to the development of contemporary heuristic approaches to learning. The authors define heutagogy as a paradigm of self-determined learning that emphasizes a high level of learner autonomy in determining the goals, content, and strategies of the learning process [2]. Central to this framework is the concept of learner agency, understood as the learner’s capacity to consciously manage their own learning, make informed decisions, and assume responsibility for their outcomes, thereby fostering critical thinking, reflection, and self-correction. The researchers also emphasize the importance of autonomy, the concept of double-loop learning, and the notion of capability, which refers to the ability to adapt to new circumstances and apply acquired knowledge in situations of uncertainty. Although the authors do not directly examine the use of artificial intelligence, their framework provides a theoretical foundation for understanding the role of AI in supporting self-determined learning through the personalization of educational processes and the enhancement of learner autonomy, reflective thinking, and learner agency. This underscores the potential of integrating heutagogy and artificial intelligence technologies in the professional preparation of future teachers [2]. The study by O. Broza, N. Chamo, L. Biberman-Shalev, and S. Bar-Tal (2026) examines the relationship between the heutagogical orientations of prospective mathematics teachers and their use of artificial intelligence technologies in the educational process. The authors conceptualize heutagogy as a model of self-determined learning that promotes learner autonomy, reflection, self-regulation, and the development of students’ digital competence. The findings identify two types of heutagogical orientation – high and low. The study demonstrates that students with a high level of heutagogical orientation use AI more frequently, exhibit stronger self- directed learning and reflective skills, and perform professional tasks with greater confidence. These results confirm the positive contribution of artificial intelligence to the development of self-determined learning. The researchers argue that AI should be employed not as a means of automating learning but as a cognitive partner that encourages questioning, the evaluation of alternative solutions, and the development of metacognitive skills. Accordingly, they recommend integrating generative artificial intelligence into the preparation of future teachers to support professional autonomy, inquiry-based thinking, and learner agency through problem-based and research-oriented learning activities. Although the empirical study was conducted in Israel, its findings are highly relevant to the analysis of teacher education in the United States, as they demonstrate the effectiveness of combining heuristic learning and artificial intelligence in fostering learner autonomy, critical thinking, and the readiness of future educators for professional practice in digitally mediated educational environments [1]. The study by U. Ramnarain, A. A. Ogegbo, M. Penn, S. M. Ojetunde, and N. Mdlalose (2024) examines the readiness of prospective science teachers to use generative artificial intelligence within the context of inquiry-based teaching. Drawing on the Theory of Planned Behavior, the authors found that the key factors influencing AI integration include a high level of AI literacy, positive attitudes toward the technology, social support, perceived usefulness, and confidence in one’s ability to use AI effectively.