Pre-service science teachers' adaptive pedagogical reasoning in AI-supported lesson plans
A case with chemical equilibrium
Bibliographic Data
| ID | 22167653 |
|---|---|
| Authors | Bongani Prince Ndlovu (0000-0002-4229-8699, University of KwaZulu-Natal), Hlologelo Climant Khoza (0000-0003-0359-6586, University of Pretoria), Doras Sibanda (0000-0001-9328-3473, University of KwaZulu-Natal) |
| Year | 2026 |
| Volume | 11 |
| Publication date | 2026-06-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1811975 |
| OpenAlex | W7164017297 |
| Language | EN |
| References cited | 43 |
Although generative artificial intelligence (AI) is increasingly being integrated into lesson planning in teacher education, little is known about how pre-service science teachers adapt AI-generated content through pedagogical reasoning for contextually responsive science teaching. This study explored how AI influences pre-service science teachers' (PSSTs) adaptive pedagogical reasoning (APR) when planning lessons on chemical equilibrium in resource-constrained contexts. Drawing on the Refined Consensus Model of Pedagogical Content Knowledge, APR was conceptualised across four dimensions: identification and anticipation of students' thinking, adaptivity in lesson design, justification of instructional decisions, and alignment with learning goals and differentiation. An exploratory mixed-methods case study was conducted with 19 third-year PSSTs in a South African university. Participants developed traditional and AI-supported lesson plans using the Rationale for Lesson Design framework. Data were analysed using an APR rubric and Rasch analysis. Findings showed that AI-supported planning enhanced anticipation of students' thinking, justification of instructional decisions, and alignment with learning goals and differentiation. However, adaptivity in lesson design remained limited. The study suggests that while AI can scaffold deeper pedagogical reasoning, teacher education programmes should explicitly support adaptive instructional decision-making
Adaptive Learning · Exploratory research · Generative grammar · Instructional design · Rasch model · Rubric · Science education · Teaching method · Educational Theory and Curriculum Studies · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
Embracing the future of Artificial Intelligence in the classroom
Artificial Intelligence in Education
The Refined Consensus Model of Pedagogical Content Knowledge in Science Education
Technological Pedagogical Content Knowledge
Using the Plan–Teach–Reflect Cycle of the Refined Consensus Model of PCK to Improve Pre-Service Biology Teachers’ Personal PCK as Well as Their Motivational Orientations
ChatGPT and Generative AI
Towards Intelligent-TPACK
Music teachers’ labeling accuracy and quality ratings of lesson plans by artificial intelligence (AI) and humans
Those Who Understand
Pedagogical Applications of Generative AI in Higher Education
Understanding the challenge of developing pedagogical reasoning in initial teacher education
Part I
Knowledge and Teaching
Constructing 21st-Century Teacher Education
| Citation velocity | historical |
|---|---|
| Highly cited | No |