AI use in training carries risks of uncritical acceptance and overreliance, including undetected conceptual errors and increased delegation of problem solving¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Included studies report conceptual errors produced by ChatGPT not identified by pre-service teachers, uncritical acceptance of generated content, and increased delegation of problem solving to the tool when tasks became more complex. → Pinto F 2026

Evidence¶

Pinto F 2026¶

Pinto F, Rodrigues RN, Brito-Costa S, Costa C, Gonçalves S, Pires NA and Martins F (2026) Artificial intelligence in initial teacher training for pre-service primary school teachers in mathematics: a systematic review. Front. Educ. 11:1934366. https://doi.org/10.3389/feduc.2026.1934366

q2 · i? · review · r2

Review synthesis of limitations and risks across included studies, which also reports that in Burgos and Tizón-Escamilla (2026) conceptual errors were not identified and in Dilling and Herrmann (2024) instances of uncritical acceptance were observed.

"Gallart et al. (2026) also found increased delegation of problem solving to the tool when tasks became more complex, while Alreiahi and Alrwaished (2025) identified overreliance on AI as one of the main challenges perceived by participants."

Discussion¶

Related Claims¶

• Reviewed literature reports risks of overreliance, AI errors in complex tasks, and students' uncritical acceptance of AI-generated outputs — possibly the same claim (merge candidate)
• Learner-level challenges of ChatGPT in STEAM IBL include over-reliance, inaccurate or irrelevant feedback, conceptual confusion, and diminished creativity — possibly the same claim (merge candidate)
• More experienced AI users showed a Boilerplate Blindspot: uncritically accepting 'trivial' AI-generated code in which later defects originated — a narrower finding that bears on this claim
• Students report negative experiences with ChatGPT including inaccurate outputs, lack of emotional connection, and risk of overreliance, which constrain competence — related
• Unstructured ChatGPT use in programming education is linked to overreliance, reduced persistence, unreliable outputs, and academic integrity risks — related
• Persistent challenges include feedback inaccuracy, learner overreliance, and limited transparency — related
• Delegating the terminal evaluation to AI is self-defeating: each hand-off returns one more output someone must accept or not — related
• Problem-solving-prepared students often accepted the AI solution uncritically or critiqued it based on their own misconceptions — a narrower finding that bears on this claim
• AI use also carries negative outcomes: technical instability, negative emotional impacts, over-reliance, and contextual contingency — related
• ChatGPT-generated text shows inaccuracy, weak logical flow, lack of critical elaboration and non-originality in an author's first-hand test — a narrower finding that bears on this claim
• ChatGPT use improves the quality, elaboration, and originality of university students' solutions to a complex ill-defined problem-solving task — related
• Graduate seminars with hands-on generative AI use and reflection increased students' comfort with AI and ability to critically assess AI outputs — related
• Pre-service teachers rate ChatGPT-generated texts higher on structure but often fail to identify AI authorship, indicating limited critical AI literacy — a narrower finding that bears on this claim