Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators

Griffin Pitts; Neha Rani; Weedguet Mildort

cs.HCApr 1, 2026

Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators

Authors: Griffin Pitts, Neha Rani, Weedguet Mildort

Organizations: North Carolina State University, Raleigh, NC, USA · University of Florida, Gainesville, FL, USA

Abstract

As generative AI systems are integrated into educational settings, students often encounter AI-generated output while working through learning tasks, either by requesting help or through integrated tools. Trust in AI can influence how students interpret and use that output, including whether they evaluate it critically or exhibit overreliance. We investigate how students' trust relates to their appropriate reliance on an AI assistant during programming problem-solving tasks, and whether this relationship differs by learner characteristics. With 432 undergraduate participants, students' completed Python output-prediction problems while receiving recommendations and explanations from an AI chatbot, including accurate and intentionally misleading suggestions. We operationalize reliance behaviorally as the extent to which students' responses reflected appropriate use of the AI assistant's suggestions, accepting them when they were correct and rejecting them when they were incorrect. Pre- and post-task surveys assessed trust in the assistant, AI literacy, need for cognition, programming self-efficacy, and programming literacy. Results showed a non-linear relationship in which higher trust was associated with lower appropriate reliance, suggesting weaker discrimination between correct and incorrect recommendations. This relationship was significantly moderated by students' AI literacy and need for cognition. These findings highlight the need for future work on instructional and system supports that encourage more reflective evaluation of AI assistance during problem-solving.

Figures & tables

Figure 1: Human-AI reliance framework [ 30 , 37 ]

Figure 2: Experimental interface with AI chatbot recommendation

Variable	1	2	3	4	5
1. Appropriate reliance (%)
2. Post-task trust	-0.42 ∗∗∗
3. AI literacy	0.03	0.15 ∗∗
4. Need for cognition	0.04	-0.00	0.32 ∗∗∗
5. Programming self-efficacy	0.02	-0.04	0.38 ∗∗∗	0.51 ∗∗∗
6. Programming literacy	-0.04	0.10 ∗	0.51 ∗∗∗	0.49 ∗∗∗	0.68 ∗∗∗

Table 1: Pearson correlations (lower triangle). Note.

{}^{*}p<.05

{}^{**}p<.01

{}^{***}p<.001

Panel of Figure 3 — Figure 3: Post-task trust predicting appropriate reliance.

Moderator	$N$	$R^{2}$	$b_{Trust}$	$p$	$b_{Mod}$	$p$	$b_{Int}$	$p$
AI literacy	432	.193	-4.62	$<.001$	0.92	.054	-1.03	.014
Need for cognition	432	.183	-4.37	$<.001$	0.53	.263	-0.99	.040
Programming self-efficacy	432	.178	-4.33	$<.001$	0.05	.921	-0.74	.121
Programming literacy	432	.174	-4.43	$<.001$	0.05	.923	-0.36	.437

Table 2: Moderation models predicting appropriate reliance from standardized trust, each moderator, and their interaction.

Panel of Figure 4 — Figure 4: Interaction models showing predicted appropriate reliance across trust at three levels of each moderator. Shaded bands represent 95% confidence intervals.

Trust in AI

Trust in artificial intelligence concerns when people and institutions should rely on AI, and how systems can make that reliance warranted and safe.

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Generative artificial intelligence (GenAI) is changing how students learn, yet the roles of course context, cognitive reliance, evaluation literacy, and early reliance remain underexplored. Using survey responses from 118 students across 12 AI-related courses at our institution, we examined differences in GenAI use and perceived learning experiences. We identified four user clusters: high-use students reporting many benefits, light users reporting less reliance and fewer benefits, and two moderate-use groups reporting different levels of benefit. We also found significant differences between free- and premium-version users, single- and multiple-tool users, and students experiencing different instructor policies. In multivariable regression models, academic benefit was associated with early reliance and academic task support; positive impact was associated with cognitive reliance, academic task support, confidence in GenAI reliability, and instructor policy; and negative impact was associated with early reliance and attitudinal change. The association between early reliance and negative impact became stronger as evaluation literacy increased. Finally, perceptions of GenAI-enhanced learning appear to reflect cognitive, performance, and self-efficacy benefits, while concerns about stress and diminished critical thinking are associated with lower perceived learning benefits. These findings suggest that institutions need better policies to address such inequities so that institutions can enable students to benefit from increasingly capable AI systems.

Abstract

Trust in AI

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