Strategic Integration of AI Chatbots in Physics Teacher Preparation: A TPACK-SWOT Analysis of Pedagogical, Epistemic, and Cybersecurity Dimensions

• URL: http://arxiv.org/abs/2507.14860v1
• Date: Sun, 20 Jul 2025 08:04:07 GMT
• Title: Strategic Integration of AI Chatbots in Physics Teacher Preparation: A TPACK-SWOT Analysis of Pedagogical, Epistemic, and Cybersecurity Dimensions
• Authors: N. Mohammadipour,
• Abstract summary: The study was conducted within a university-level capstone course on innovative tools for physics instruction.<n>Results highlight internal strengths such as enhanced information-seeking behavior, scaffolded pedagogical planning, and support for symbolic reasoning.<n> External opportunities were found in promoting inclusive education, multilingual engagement, and expanded zones of proximal development.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Human-Centric Approach to Explainable AI for Personalized Education [1.0878040851638]
  This thesis aims to bring human needs to the forefront of eXplainable AI (XAI) research.<n>We propose four novel technical contributions in interpretability with a multimodal modular architecture.<n>Our work lays a foundation for human-centric AI systems that balance state-of-the-art performance with built-in transparency and trust.
  arXiv  Detail & Related papers  (2025-05-28T16:23:48Z)
• KERAIA: An Adaptive and Explainable Framework for Dynamic Knowledge Representation and Reasoning [46.85451489222176]
  KERAIA is a novel framework and software platform for symbolic knowledge engineering.<n>It addresses the persistent challenges of representing, reasoning with, and executing knowledge in dynamic, complex, and context-sensitive environments.
  arXiv  Detail & Related papers  (2025-05-07T10:56:05Z)
• Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation [0.0]
  This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools.<n>We integrate cognitive assessment frameworks, linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools.
  arXiv  Detail & Related papers  (2025-05-01T06:36:21Z)
• Beyond Tools: Generative AI as Epistemic Infrastructure in Education [0.0]
  generative AI rapidly integrates into educational infrastructures worldwide.<n>This paper investigates how AI systems function as epistemic infrastructures in education.<n>It analyzes their impact on teacher practice across three dimensions.
  arXiv  Detail & Related papers  (2025-04-09T14:35:30Z)
• Form-Substance Discrimination: Concept, Cognition, and Pedagogy [55.2480439325792]
  This paper examines form-substance discrimination as an essential learning outcome for curriculum development in higher education.<n>We propose practical strategies for fostering this ability through curriculum design, assessment practices, and explicit instruction.
  arXiv  Detail & Related papers  (2025-04-01T04:15:56Z)
• CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation [13.352385179504482]
  In cybersecurity education, accuracy and safety are paramount, and systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate.<n>We introduce CyberBOT, a question-answering robot that incorporates contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology.<n>CyberBOT has been deployed in a large graduate-level course at Arizona State University, where more than one hundred students actively engage with the system through a dedicated web-based platform.
  arXiv  Detail & Related papers  (2025-04-01T03:19:22Z)
• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.<n>We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.<n>We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models [48.559185522099625]
  Planning is a crucial element of both human intelligence and contemporary large language models (LLMs) This paper investigates the emergence of planning capabilities in Transformer-based LLMs via their next-word prediction mechanisms.
  arXiv  Detail & Related papers  (2024-05-15T09:59:37Z)
• Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
  We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement. We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
  arXiv  Detail & Related papers  (2024-03-06T18:29:18Z)
• A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks.<n>PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.