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The advent of Chat-GPT, an AI-driven technology, is reshaping various sectors, particularly higher education. This study, merging the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Protection Motivation Theory (PMT), explores the relationship between technology adoption and protection motivation in higher education, addressing the concept of “the (un)knowledgeable, the (un)skilled.” Through Structural Equation Modeling, findings from Indonesian participants (N = 445), including students and lecturers, reveal that perceived benefits such as task efficiency, hedonic motivation, performance expectancy, and effort expectancy predominantly motivate Chat-GPT use. Risk perception leans more on perceived vulnerability than severity, with insignificant impact from response cost. Response efficacy and self-efficacy significantly determine intention. Insight from moderating effect of educational level suggest that doctoral-level users demonstrate increased Chat-GPT usage, and gender plays a role in response efficacy and task efficiency. Both educational level and gender actively influence users’ intentions and actual usage behavior. These insights guide educators, policymakers, and institutions in ethically integrating AI, managing evolving risk perceptions, and empowering users in the dynamic AI landscape of higher education. Policymakers (e.g., university boards) are urged to dexterity an ethical framework and authorize in user efficacy to ensure equitable access and benefits from Chat-GPT in education.
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Stored only when both papers are in the corpus — 33 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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