Conceptualizing "Noaiphobia": Psychophysiological stress during unaided versus AI-assisted writing in an exploratory pilot study
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Published on: March 8, 2018
Omar Trabelsi1,2,3, Mustapha Bouchiba4, Mohamed Yaakoubi3
1The High Institute of Applied Studies in Humanities of Mahdia, Department of Education Sciences, University of Monastir, Mahdia, Tunisia.
Abstract:
The rapid integration of generative AI into academic workflows has raised concerns about psychological dependence and about what happens when access is lost. This paper proposes "Noaiphobia" as an exploratory construct: the acute psychophysiological stress response associated with the unavailability of AI support during task execution. We tentatively frame it as arising from three processes: withdrawal-like reactions to habitual reliance, an increase in intrinsic cognitive load once an external scaffold is removed, and a threat appraisal in which task demands are perceived to exceed internal coping resources. As an initial empirical probe, an exploratory pilot study was conducted with 18 Tunisian sports science students who were daily AI users. In a within-subjects design with a fixed condition order, participants completed a short writing task in French first without AI and then with AI assistance; mean heart rate and perceived task stressfulness (visual analogue scale) were recorded in each condition. Resting heart rate had been measured in a separate session one week earlier (62.98 ± 4.78 bpm). Mean heart rate (93.10 ± 9.36 vs. 77.15 ± 5.99 bpm; MD = 15.95 bpm, 95% CI [11.56, 20.34], p < 0.001, dz = 1.81, 95% CI [1.04, 2.56]) and perceived stressfulness (73.06 ± 8.40 vs. 28.06 ± 7.44 mm; MD = 45.00 mm, 95% CI [41.53, 48.47], p < 0.001, dz = 6.44, 95% CI [4.24, 8.63]) were higher in the no-AI condition. These results offer preliminary evidence of a condition difference consistent with the proposed construct, but the fixed order and the greater effort demanded by unaided writing mean a withdrawal mechanism cannot be isolated. The study is best read as conceptual groundwork, and we outline the counterbalanced, effort-matched designs with direct measures of AI reliance needed to test the construct.