From Enhancement to Over-Reliance: A Mixed-Method Study of Generative AI and Sustainable Learning Performance

Synthesis: Gao, Sun, and Khan (2026) developed a dual-pathway model examining both the positive and negative effects of generative AI use on sustainable learning performance, integrating AI literacy, self-regulated learning, cognitive offloading, and individual differences (polychronicity). Using a mixed-method design with three-wave time-lagged survey data from 623 Chinese university students plus educator interviews, they found that AI literacy significantly enhances critical AI evaluation, which — along with self-regulated learning — promotes effective AI use. Effective AI use positively influences sustainable learning performance but also increases AI over-reliance, which negatively affects learning outcomes, with polychronicity moderating key relationships.

Key Findings

AI literacy significantly enhances critical AI evaluation, which, along with self-regulated learning, promotes effective AI use.

Effective AI use positively influences sustainable learning performance but also increases AI over-reliance, which negatively affects learning outcomes — the dual, or two-edged, nature of AI use.

Polychronicity (multitasking tendency) moderates key relationships, shaping both AI dependency and learning effectiveness: students high in polychronicity are at greater risk of over-reliance.

The qualitative findings from educator interviews support and explain the quantitative results, highlighting the behavioral mechanisms underlying AI-supported learning.

The study's central contribution is recognizing two parallel processes coexisting in AI use: creating sustainable learning opportunities while also fostering cognitive over-dependency on AI.

Study Design & Method

The study employed a mixed-method design combining three-wave time-lagged survey data from 623 university students in China with qualitative interviews with educators. The quantitative strand used PLS-SEM to test the dual-pathway model linking AI literacy, critical AI evaluation, self-regulated learning, effective AI use, AI over-reliance, and sustainable learning performance, with polychronicity as a moderator. The qualitative strand used thematic analysis of educator interviews to deepen and explain the observed relationships. The theoretical framework integrated the AI literacy framework, Self-Regulated Learning Theory, and Cognitive Offloading Theory.

Implications for AI in Education

The findings reframe AI's impact on learning as inherently dual — capable of both enhancement and Over Reliance — and locate the determining factors in how students engage with the technology. For educators, this argues for building AI Literacy (the capacity to evaluate AI output critically and use it meaningfully) and Self Regulated Learning skills so students use AI as a scaffold rather than a substitute, and for attending to individual differences such as polychronicity that shape dependency risk. The study connects AI use to Cognitive Offloading risks, warning that habitual delegation of cognitive processing can erode the internal effort needed for deep learning, and offers implications for designing interventions that maximize the enhancement pathway while mitigating over-reliance.

Limitations

The cross-sectional, self-report survey design limits causal inference despite the time-lagged structure. The sample is drawn from Chinese university students, bounding generalizability. Polychronicity and the AI-literacy/AI-evaluation constructs rely on self-report operationalizations. The qualitative strand, while informative, is limited to educator perspectives and does not directly capture students' behavioral mechanisms.

Connected Concepts

Generative AI

Over Reliance

AI Literacy

Self Regulated Learning

Cognitive Offloading

AI Misuse Learning Harm

Higher Ed

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Citation

Gao, L., Sun, Y., & Khan, S. U. (2026). From enhancement to over-reliance: A mixed-method study of generative AI and sustainable learning performance.