Are Students Dependent on AI in Writing Courses? Analyzing Factors Influencing Dependence on Generative AI Through the I-PACE Model

Synthesis: Liu, Zhuang, and Wang (2026) apply the I-PACE model of problematic use to generative AI dependence in undergraduate academic writing. In a mixed-methods study at two Chinese universities — a survey analyzed with structural equation modeling plus eight interviews — academic stress emerges as the strongest predictor of dependence, AI Literacy as a protective factor, and social influence as consequential only through perceived Trust. Students knew AI's risks yet kept using it strategically, citing efficiency, peer pressure, and ambiguous institutional rules.

Key Findings

• Academic stress is the strongest driver of dependence. Students under the heaviest workload pressure were most likely to lean on AI to produce writing, and had trouble separating the academic task from the assistance.
• AI literacy is a protective factor. Students who understood how AI works, its limits, and its social consequences were less prone to dependence, apparently because literacy supported critical distance.
• Trust, not usefulness, is the pathway to dependence. Students who could name AI's failures still trusted its output and depended on it more; awareness of error did not become skepticism in practice.
• Peer influence acts through trust, not directly. Social influence had no significant direct effect on dependence, but strongly predicted perceived trust, which in turn predicted dependence — conformity matters mainly by making AI feel credible.
• Academic self-efficacy does not by itself reduce stress. Contrary to prior work, confidence in one's academic ability was unrelated to stress until perceived usefulness entered the picture; the buffering effect appeared only for students who saw AI as useful.
• Students use AI strategically and call it defensible. Interviewees described mixing tools and rewriting output to evade detection, framed as responsible use, and blamed the absence of any clear institutional definition of compliant AI use.

Study Design & Method

A sequential mixed-methods design in a required research-methods writing course for journalism majors. In the quantitative phase, 266 undergraduates at universities in Fujian and Guangdong completed an online questionnaire; after screening for incomplete, straight-line, and implausibly fast responses, 229 were analyzed. The survey measured academic self-efficacy, AI literacy, academic stress, perceived trust, social influence, and perceived usefulness with adapted five-point Likert scales, back-translated and pre-tested. Relationships were estimated through structural equation modeling, with bootstrapping for non-normal data and a common-method-bias check.

The qualitative phase drew on open-ended survey responses and semi-structured interviews with eight volunteers selected for heterogeneity in academic performance. Interviews covered their experience of AI-assisted writing, motives for heavy use, and their sense of dependence; transcripts were analyzed thematically by two researchers who coded independently.

Implications

• Address risk and protection together. Curbing dependence means lowering excessive academic stress while building AI Literacy — stress drives use, literacy restrains it.
• Target peer norms and trust calibration, not just tool skills. Because dependence forms through a social-influence to trust to behavior route, prompting workshops miss the mechanism that produces it.
• Write explicit AI-use rules. Students filled the policy vacuum with improvised, self-justifying strategies; clear definitions of compliant use, and assessment that makes responsible use the easier option, would remove that ambiguity.

Limitations

• Cross-sectional, single-culture sample of Chinese college students in academic writing courses; causal claims and generalizability to other settings are limited.
• Self-reported measures of dependence, stress, and literacy; no objective usage data, so dependence is measured as perception rather than behavior.
• Small qualitative sample of eight interviewees, limiting the breadth of thematic claims.
• I-PACE was designed for hedonic apps such as gaming and social media; extending it to a study tool carries assumptions about what dependence means in academic work.

Connected Concepts

• Cognitive Offloading — dependence is framed as habitual offloading of core writing work to AI.
• Academic Integrity — policy ambiguity let students rationalize strategic AI use as compliant.
• Writing — the study setting is an undergraduate academic-writing course.
• AI Literacy — the protective factor against dependence in the model.
• AI Detection — students rewrote output specifically to evade detection.
• Self-Efficacy — academic self-efficacy failed to predict stress directly, against expectation.
• Higher Education — the population studied, and the policy context of the findings.
• Teaching — instructors' guidance and enforcement shaped students' reading of the rules.

Connected Articles

• Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-Solving — Over-reliance on AI in collaborative learning contexts
• PAUSE: A Privacy-Preserving Self-Reflection Tool for AI-Associated Cognitive Offloading — A privacy-preserving tool for AI-associated cognitive offloading
• Generating In-Context, Personalized Feedback for Intelligent Tutors with Large Language Models — LLM feedback and learner interaction in writing contexts

Citation

Liu, L., Zhuang, M., & Wang, J. (2026). Are Students Dependent on AI in Writing Courses? Analyzing Factors Influencing Dependence on Generative AI Through the I-PACE Model. Frontiers in Psychology, 17, 1905037.