Differentiated AI literacy training matched to acceptance profiles, with interventions beyond operational skills for Resistant Skeptics¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article proposes that teacher education institutions use the empirically identified user types as the basis for AI literacy training design. As stated in the conclusions, "the study proposes a differentiated AI literacy training framework, emphasizing that profiles such as Resistant Skeptics require targeted interventions that extend beyond operational skills training." This follows from the finding that Resistant Skeptics already perceive ChatGPT as easy to use, so skills training alone would not address their negative evaluative beliefs and perceived social norms.

Design Implications¶

Context¶

Requirements¶

• Identification of learner profiles via person-centered analysis before designing training, consistent with adult learning theory's emphasis on learner heterogeneity

Constraints¶

• Current AI literacy programs are often designed as uniform interventions that rarely take disciplinary background or individual differences in motivation and needs into account, which the article identifies as limiting the breadth and depth of AI literacy development

Target Learners¶

• pre-service teachers in teacher education programs, in both STEM and non-STEM disciplines

Target Learning Goals¶

• AI literacy development and acceptance of generative AI tools for learning and future teaching

Affordances¶

• Diffusion User Typology Ai Acceptance

Claims¶

• Latent profile analysis of pre-service teachers' ChatGPT acceptance yields four highly distinguishable profiles, with Pragmatic Evaluators the largest (47.66%) [+W]

Related Strategies¶

• Differentiated, profile-matched university AI-readiness and career-education support instead of uniform AI-literacy programs
• Typology-differentiated AI adoption policy interventions for education systems

Examples¶

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Key Sources¶

• Pei-Hua Chen, Hsin-Yu Lee, Yueh-Min Huang and Ting-Ting Wu. (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. https://doi.org/10.1186/s40594-026-00634-x