Unpacking the Heterogeneity of Pre-service Teachers' ChatGPT Acceptance: A Latent Profile Analysis Across STEM and Non-STEM Disciplines

Synthesis: This person-centered latent profile analysis (LPA) of N = 128 Taiwanese pre-service teachers (68 STEM, 60 non-STEM) identifies four distinct ChatGPT-acceptance profiles — Pragmatic Evaluators (47.66%), Technology Pioneers (26.56%), Resistant Skeptics (14.06%), and Environmental Observers (11.72%) — and shows that disciplinary background strongly shapes profile membership (Cramer's V = 0.532). Critically, Resistant Skeptics report high perceived ease of use but very low behavioral intention, proving technical ease does not guarantee adoption. The study proposes a differentiated AI Literacy training framework for teacher education.

Key Findings

1. Four distinguishable acceptance profiles (entropy = 0.985). Person-centered LPA (using TAM and UTAUT2 constructs) revealed Pragmatic Evaluators (47.66%), Technology Pioneers (26.56%), Resistant Skeptics (14.06%), and Environmental Observers (11.72%).
2. The ease-of-use ≠ intention paradox. Resistant Skeptics had relatively high perceived ease of use yet very low behavioral intention — demonstrating that perceived technical ease does not guarantee adoption, and implying resistance is driven by attitudes/values rather than capability.
3. Discipline shapes acceptance. STEM pre-service teachers concentrated in Technology Pioneers (47.1%), while non-STEM teachers were overrepresented in Environmental Observers and Resistant Skeptics (χ² = 36.20, p < .001, Cramer's V = 0.532).
4. Profiles differ strongly in behavioral intention (F = 109.00, p < .001, η² = 0.726), confirming the profiles are meaningfully distinct in downstream intention.
5. A differentiated AI-literacy framework. Profiles such as Resistant Skeptics require targeted interventions that extend beyond operational skills training — addressing attitudes, trust, and contextual factors.

Implications

For teacher education and Faculty Development, this study shows that AI acceptance among future teachers is not a single dimension but a heterogeneous set of profiles shaped by discipline. One-size-fits-all training fails; differentiated AI Literacy interventions must target the distinct needs of Resistant Skeptics (attitudinal), Environmental Observers (contextual support), and Technology Pioneers (advanced practice). The ease-of-use/intention paradox underscores that operational training alone is insufficient for teacher adoption of generative AI — a critical insight for scaling AI-integration in STEM and non-STEM classrooms.

Connected Concepts

• Teacher Education
• Faculty Development
• AI Literacy
• Technology Acceptance Model
• Generative AI
• LLM
• STEM Education
• Higher Ed

Connected Articles

• AI Acceptance Preservice Science Teachers 2026 — AI acceptance of pre-service science teachers
• Teacher Education AI Literacy Sdt 2026 — Teacher education AI literacy
• GenAI Literacy Training Teacher Education Dbr 2026 — GenAI literacy training in teacher education
• AI Changing Teaching Workflows — AI changing teaching workflows
• Enright Staff Perspectives GenAI 2026 — Staff perspectives on GenAI

Citation

Chen, P.-H., Lee, H.-Y., Huang, Y.-M., & Wu, T.-T. (2026). Unpacking the heterogeneity of pre-service teachers' ChatGPT acceptance: a latent profile analysis across STEM and non-STEM disciplines. International Journal of STEM Education, 13, 33.