Digital self-Efficacy as a foundation for a generative AI usage framework in faculty's professional practices

TL;DR

This study investigates how digital self-efficacy influences higher education faculty's adoption of generative AI, proposing a tailored usage framework based on self-efficacy profiles and sociotechnical configurations.

Contribution

It introduces a validated digital self-efficacy scale and a differentiated GAI usage framework tailored to faculty profiles, advancing understanding of AI adoption in education.

Findings

Identified three faculty user profiles: Engaged, Reflective Reserved, Critical Resisters.

Established a significant link between self-efficacy levels and GAI usage patterns.

Validated a three-dimensional digital self-efficacy measurement scale.

Abstract

This research explores the role of digital self-efficacy in the appropriation of generative artificial intelligence (GAI) by higher education faculty. Drawing on Bandura's sociocognitive theory and Flichy's concept of usage framework, our study examines the relationships between levels of digital self-efficacy and GAI usage profiles. A survey of 265 faculty members identified three user profiles (Engaged, Reflective Reserved, Critical Resisters) and validated a three-dimensional digital self-efficacy scale. Results reveal a significant association between self-efficacy profiles and GAI appropriation patterns. Based on these findings, we propose a differentiated usage framework integrating four sociotechnical configurations, appropriation trajectories adapted to self-efficacy profiles, and personalized institutional support mechanisms.