Prior research identifies motivational, cognitive, affective, contextual, and ethical factors influencing GenAI adoption among instructors and students¶

Claim · All claims
Evidence · 1 study · design r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The literature review reports that perceived value, hedonic motivation, and habit predict GenAI intention; knowledge and awareness foster positive dispositions; optimism and enjoyment positively influence perceptions while anxiety and perceived cost negatively impact them; and ethical concerns around bias, privacy, and inaccurate responses negatively affect perceptions. → Stella Y. Kim 2026

Evidence¶

Stella Y. Kim 2026¶

Stella Y. Kim, Ji Yae Bong, Daniel Maxwell, Beth Oyarzun. (2026). Development and validation of Generative Artificial Intelligence Readiness and Perception (GenAI-RP) Scale for faculty and students in higher education. Education Tech Research Dev. https://doi.org/10.1007/s11423-026-10713-z

q1 · i? · design · r2

This is the article's narrative review of prior studies, attributed to the cited works (e.g., Strzelecki 2024, Yilmaz et al. 2023, Lemke et al. 2023, Wang et al. 2023), not a new empirical analysis. The review organizes factors into motivational, cognitive, affective, contextual, and ethical categories.

"Motivational factors such as perceived value (Wigfield & Eccles, 2000), hedonic motivation (Kang et al., 2015; Kasneci et al., 2023; Strzelecki, 2024), and habit (Strzelecki, 2024) are a significant predictor of GenAI intention and utilization."

Discussion¶

Related Claims¶

• Contextual and moderating variables (instructor guidance, prior experience, instructor endorsement) show significant but weaker effects on GenAI acceptance than student-centered perceptual factors — a narrower finding that bears on this claim
• Hedonic motivation and habit were the strongest predictors of behavioral intention, and habit also substantially predicted use behavior — a narrower finding that bears on this claim
• Effort expectancy is associated with AI use only indirectly through competence, whereas hedonic motivation shows a direct positive association — related
• Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument). — related
• No previously validated GenAI perception or readiness instrument was designed for both instructors and students in AI-enhanced higher education — related