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Technology Acceptance Model

● high · created 2026-08-18 · updated 2026-08-20 · technology-acceptance-model, generative-ai, student-experience, higher-ed, ai-literacy, research-methods-aied, adoption

The Technology Acceptance Model (TAM), proposed by Davis (1989), explains and predicts users' adoption of new technologies through two core beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), which shape users' Attitude (ATT) toward the technology. Attitude then predicts Behavioral Intention (BI) to use it, which ultimately influences actual usage behavior. Grounded in information systems theory, TAM has become the dominant framework for studying generative AI adoption in education.

Core Structure

The classic TAM posits that PU and PEOU jointly determine attitude, which drives behavioral intention and, in turn, actual use. In educational GenAI research, TAM is frequently extended with additional predictors — including AI Literacy, Trust, social influence, self-determination, and critical use — to capture the complexity of AI acceptance beyond simple uptake.

Applications in the Wiki

TAM is applied across the wiki to model student and teacher adoption of AI tools:

  • Critical use extension: Nguyen et al. extended TAM with the construct of critical use for engineering/CS students, finding that attitudes and critical use directly predict intention, which then predicts reliance across understanding, assessment, programming, and engineering-project domains — and that critical use safeguards against over-reliance.
  • Unified socio-cognitive model: Asag & Al Mamun integrated TAM with UTAUT to model engineering students' GenAI adoption in Bangladesh, showing that job relevance, result demonstrability, and subjective norms shape acceptance (explaining 64% of usage variance).
  • Regulatory competence critique: Kim argues that adoption-centered TAM models treat use as a stable decision, whereas effective AI use is an ongoing process of judgment, revision, and selective uptake — framing AI Literacy as regulatory competence and Critical Thinking rather than acceptance.

Limits and Extensions

While TAM is effective for predicting uptake, it is less well suited to explaining how students work with AI output once generated. Reviews of GenAI in higher education increasingly note that TAM alone is insufficient, prompting integration with frameworks such as UTAUT and Self-Determination Theory, and the addition of post-adoption constructs like critical use, reliance, and evaluative judgment.

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