Paper Type

ERF

Abstract

The rapid integration of generative AI into higher education has introduced a psychological phenomenon we term AI fatalism: the belief that AI-driven outcomes are predetermined and that individual effort is instrumentally irrelevant. Drawing on learned helplessness theory and its attributional reformulation, we propose that AI fatalism negatively affects students’ perceived job outcomes through three parallel mechanisms—reduced motivation, diminished self-efficacy, and heightened anxiety. We present a two-study design to examine these relationships. Study 1 uses a cross-sectional survey with parallel mediation modeling to test the relationships. Study 2 employs a between-subjects experiment with a two-task sequential design to establish causal evidence by inducing AI fatalism through social comparison with AI-generated outputs. Data collection is currently underway. This research contributes a novel construct to the IS and education literatures, extends learned helplessness theory to technology contexts involving misattributed rather than objective constraints, and offers actionable implications for how institutions frame AI in learning environments.

Paper Number

1748

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Aug 15th, 12:00 AM

AI Fatalism’s Impact on Perceived Job Outcomes

The rapid integration of generative AI into higher education has introduced a psychological phenomenon we term AI fatalism: the belief that AI-driven outcomes are predetermined and that individual effort is instrumentally irrelevant. Drawing on learned helplessness theory and its attributional reformulation, we propose that AI fatalism negatively affects students’ perceived job outcomes through three parallel mechanisms—reduced motivation, diminished self-efficacy, and heightened anxiety. We present a two-study design to examine these relationships. Study 1 uses a cross-sectional survey with parallel mediation modeling to test the relationships. Study 2 employs a between-subjects experiment with a two-task sequential design to establish causal evidence by inducing AI fatalism through social comparison with AI-generated outputs. Data collection is currently underway. This research contributes a novel construct to the IS and education literatures, extends learned helplessness theory to technology contexts involving misattributed rather than objective constraints, and offers actionable implications for how institutions frame AI in learning environments.

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