Date
Publisher
arXiv
This study investigates the strategic and epistemically responsible
integration of AI-powered chatbots into physics teacher education by employing
a TPACK-guided SWOT framework across three structured learning activities.
Conducted within a university-level capstone course on innovative tools for
physics instruction, the activities targeted key intersections of
technological, pedagogical, and content knowledge (TPACK) through
chatbot-assisted tasks: simplifying abstract physics concepts, constructing
symbolic concept maps, and designing instructional scenarios. Drawing on
participant reflections, classroom artifacts, and iterative feedback, the
results highlight internal strengths such as enhanced information-seeking
behavior, scaffolded pedagogical planning, and support for symbolic reasoning.
At the same time, internal weaknesses emerged, including domain-specific
inaccuracies, symbolic limitations (e.g., LaTeX misrendering), and risks of
overreliance on AI outputs. External opportunities were found in promoting
inclusive education, multilingual engagement, and expanded zones of proximal
development (ZPD), while external threats included prompt injection risks,
institutional access gaps, and cybersecurity vulnerabilities. By extending
existing TPACK-based models with constructs such as AI literacy,
prompt-crafting competence, and epistemic verification protocols, this research
offers a theoretically grounded and practically actionable roadmap for
embedding AI in STEM teacher preparation. The findings affirm that, when
critically scaffolded, AI chatbots can support metacognitive reflection,
ethical reasoning, and instructional innovation in physics education if
implementation is paired with digital fluency training and institutional
support.
Study at a glance
- Question: How do future physics teachers experience and evaluate AI chatbots for information seeking, lesson planning, and conceptual tasks?
- Setting and data: University-level elective capstone course titled "Innovative Tools in Physics Education" for pre-service physics teachers, Farhangian University, Tehran, Iran; participant reflections, observations, and transcripts.
- Method: Qualitative design using thematic analysis and a TPACK-guided SWOT analysis across three structured course activities.
Why it matters
Physics education involves abstract concepts and symbolic logic, yet little is known about how future physics teachers use AI chatbots, verify their outputs, or manage cybersecurity and epistemic risks in instructional planning.
Key findings
- Pre-service physics teachers found that AI chatbots effectively supported conceptual simplification, lesson structuring, concept mapping, and basic simulation coding.
- Chatbots regularly exhibited weaknesses including domain-specific errors, oversimplifications, and misrendered symbolic notation, which required students to cross-check outputs with textbooks or peers.
- Participants noted opportunities for multilingual access and differentiated learning, but raised threats regarding overreliance, prompt injection attacks, uneven access, and lack of institutional training.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
