A systematic review reports that over-reliance on AI dialogue systems fosters complacency and dependency and may weaken students' decision-making¶

Claim · All claims
Evidence · 1 study (2 entries) · review r2 · q2
Currency · newest study 2024 · checked against its sources 2026-10-17 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? In a study of 16 college students, AI dialogue systems decreased research and information retrieval time, but over-reliance fostered complacency and undue dependency. → Chunpeng Zhai 2024 q2 i? In a study of 15 non-native English speaking EFL learners, the review says oversimplified AI explanations may increase reliance on AI, which it says undermines learners' independent analysis and impairs decision-making skills. → Chunpeng Zhai 2024 (2)

Evidence¶

Chunpeng Zhai 2024¶

Chunpeng Zhai, Santoso Wibowo and Lily D. Li. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments. https://doi.org/10.1186/s40561-024-00316-7

q2 · i? · review · r2

Study of 16 college students' interactions with AI dialogue systems for academic tasks, reported in the review's decision-making section. The review reports efficiency gains in research and information retrieval alongside complacency and undue dependency; concerns about plagiarism, decreased creativity, data bias, security and discrimination also emerged.

"Duhaylungsod and Chavez (2023) investigated 16 college students' interactions with AI dialogue systems for academic tasks. The results indicated that AI dialogue systems efficiently decreased the time dedicated to research and information retrieval. This over-reliance fostered complacency and an undue dependency on AI dialogue systems."

Chunpeng Zhai 2024 (2)¶

Chunpeng Zhai, Santoso Wibowo and Lily D. Li. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments. https://doi.org/10.1186/s40561-024-00316-7

q2 · i? · review · r2

Study of 15 non-native English speaking EFL learners using AI dialogue systems for text paraphrasing, reported in the review's decision-making section. The review states that when explanations are overly simplified this "may result in an increased reliance on AI", undermining learners' ability to analyze information independently and impairing decision-making skills.

"It reveals that the main difficulty arises from the lack of comprehensive explanations accompanying AI-generated paraphrases. This deficiency makes it challenging for learners to grasp the context and verify the accuracy of the reformulated content."

Studies bearing on this claim¶

When learners make decisions with AI support, does the support erode their own judgement, and do reflection prompts protect it? This table gathers the studies behind this claim and the 4 narrower claims that bear on it: 4 distinct studies (1 review, 1 causal, 1 theoretical, 1 design), q1–q3, 1 of 4 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Learner Variables¶

• Critical Thinking — outcome: instruction changes it

Related Claims¶

• Over-reliance on AI dialogue systems reduces students' critical and analytical thinking skills, especially when they become overly dependent on AI-generated content — related
• Over-reliance on AI scaffolding risks learned helplessness, so focus should shift to fostering self-regulated AI interaction — related
• Within the AI-assisted group, the frequency of checking the AI's claims against sources predicted decision-making gains, with no students flagged for over-reliance — related
• Longitudinal studies of trust and reliance in AI advice examine whether users rely appropriately over time but not whether independent reasoning capacity is maintained — related
• Open ChatGPT support increases college students' final decision confidence relative to independent decision-making, and reflection attenuates the boost — related
• Conflict with a machine-learning decision support system's output induces deeper reflection than confirmation — related
• Reflective strategies such as cognitive forcing, pre-commitment, and uncertainty communication reduce overreliance on AI and improve decision quality, but have been evaluated only as short-term single-session interventions — related
• Excessive dependence on AI may reduce human interaction and dehumanize adult education — related
• The effectiveness of reflective AI-engagement mechanisms varies across users, depending on Need for Cognition, expertise, domain familiarity, and task difficulty — related