TruaceTracing the truth around AIWednesday, August 5, 2026
TRV-2026-0548Certified recordPeer-reviewed

Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance

Abstract With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of supports from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. In particular, there has been a surge of academic interest in human‐AI collaboration and hybrid intelligence in learning. The concept of hybrid intelligence is still at a nascent stage, and how learners can benefit from a symbiotic relationship with var…

Policy · The Trace — both readings · certified 2026-07-24 · v1 · article view · machine-readable

Current reading — gain

Learners supported by ChatGPT showed higher short-term essay score improvement compared to other support conditions.

Current reading — problem

Learners using ChatGPT may develop dependence on the tool and exhibit metacognitive laziness that hinders self-regulation and deep engagement.

What this doesn’t fix

Findings come from a single lab-based writing task with 117 university students, limiting generalizability to other tasks, contexts, and populations.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0548, v1: “Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance.” Truvace, 2026-07-24. /record/TRV-2026-0548 (accessed at citation time). sha256 d14ab7a267c65b00

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1d14ab7a267c6

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0548 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.