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<p>Artificial intelligence (AI) influences learning processes, student activities, and the organization of teaching, both in general and specifically in science education. Research indicates that the way AI will affect science learning primarily depends on how it is implemented in teaching. This study examines the ability of preservice biology teachers (PSBTs) to independently develop pedagogical AI agents (TDP-AI agents), most often in the form of chatbots, tailored to teaching goals, content, and students’ needs. This empirical study, based on a mixed-methods research approach and applying the ICAP theoretical framework, analyzed 54 lesson plans. In addition, the study explored PSBTs’ perceptions regarding the development and contribution of TDP-AI to biology teaching. The results reveal three patterns of AI agent application in lesson plans: (a) intensive use of TDP-AI agents across all lesson phases, promoting students’ cognitive engagement, (b) moderate and selective use focused on core activities, and (c) limited use mainly in introductory segments. Five thematic areas reflect PSBTs’ perspectives: (1) personalization and flexibility of learning, (2) enhancement of student motivation and engagement, (3) development of teachers’ digital and pedagogical competencies, (4) technical and resource-related challenges in agent development, and (5) pedagogical-methodological barriers in designing student–AI interactions. The findings emphasize the need for systematic support for preservice teachers in developing digital tools and building pedagogical approaches to ensure AI is used ethically and effectively in science education.</p>

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Empowering Preservice Biology Teachers as Designers of Pedagogical AI Agents: Moving Beyond Data Feeding toward Pedagogical Design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Branko Anđić,
  • Christoph Helm,
  • Robert Weinhandl,
  • Mirjana Maričić,
  • Antonia Radlmair,
  • Valentina Bleckenwegner,
  • Martin Ebner

摘要

Artificial intelligence (AI) influences learning processes, student activities, and the organization of teaching, both in general and specifically in science education. Research indicates that the way AI will affect science learning primarily depends on how it is implemented in teaching. This study examines the ability of preservice biology teachers (PSBTs) to independently develop pedagogical AI agents (TDP-AI agents), most often in the form of chatbots, tailored to teaching goals, content, and students’ needs. This empirical study, based on a mixed-methods research approach and applying the ICAP theoretical framework, analyzed 54 lesson plans. In addition, the study explored PSBTs’ perceptions regarding the development and contribution of TDP-AI to biology teaching. The results reveal three patterns of AI agent application in lesson plans: (a) intensive use of TDP-AI agents across all lesson phases, promoting students’ cognitive engagement, (b) moderate and selective use focused on core activities, and (c) limited use mainly in introductory segments. Five thematic areas reflect PSBTs’ perspectives: (1) personalization and flexibility of learning, (2) enhancement of student motivation and engagement, (3) development of teachers’ digital and pedagogical competencies, (4) technical and resource-related challenges in agent development, and (5) pedagogical-methodological barriers in designing student–AI interactions. The findings emphasize the need for systematic support for preservice teachers in developing digital tools and building pedagogical approaches to ensure AI is used ethically and effectively in science education.