Gen-AI hallucinates sources and values and produces convincing but incorrect explanations, risking student misinformation¶
Claim · All claims
Evidence · 1 study (2 entries) · designr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Without source files, ChatGPT generated false information such as reviewing a nonexistent paper; even with the paper supplied it fabricated details like sample size. → Ivory 2026
q2 i? Incorrect AI answers came with explanations convincing enough to mislead students using Gen-AI as a learning or revision aid. → Ivory 2026 (2)
Evidence¶
Ivory 2026¶
Ivory, M., Finnerty, S., Dunn, K., Philpot, R., & Warmelink, L. (2026). The Integrity of Psychology Assessments in the AI Age: A Critical Examination. Journal of Academic Ethics. https://doi.org/10.1007/s10805-026-09729-0
q2 · i? · design · r2
In the paper review assessment (PSYC305), basic prompting reviewed a nonexistent paper with an unresolvable DOI; with the paper uploaded, ChatGPT fabricated the sample size, stating 378 participants whereas the paper reported 329.
"Further Gen-AI is prone to the generation of incorrect information and the hallucination of sources and references that do not exist."
Ivory 2026 (2)¶
Ivory, M., Finnerty, S., Dunn, K., Philpot, R., & Warmelink, L. (2026). The Integrity of Psychology Assessments in the AI Age: A Critical Examination. Journal of Academic Ethics. https://doi.org/10.1007/s10805-026-09729-0
q2 · i? · design · r2
Observation from the WBA transcripts (e.g. a developmental psychology question in PSYC205 WBA 6 where the correct answer was (a) but ChatGPT answered 'd. All of the above' with a plausible justification); the authors warn students without prior knowledge may become misinformed.
"when the Gen-AI provided incorrect answers, the associated explanations were often convincing, particularly on content outside the expertise of the researchers."
Discussion¶
Related Claims¶
- Poorly grounded GenAI self-study risks fabricated content and inferior learning relative to curated resources — possibly the same claim (merge candidate)
- Reviewed literature reports risks of overreliance, AI errors in complex tasks, and students' uncritical acceptance of AI-generated outputs — a broader claim this one bears on
- Phase VI AI-enabled systems face six documented challenges: explainability, hallucination risk, prompt sensitivity, computational cost, validation complexity, and limited large-scale evidence — a broader claim this one bears on
- Zero-shot LLM errors follow four recurring patterns: over-interpretation, failure to detect relevant information, hallucination, and failure to generate a response — related
- Students report negative experiences with ChatGPT including inaccurate outputs, lack of emotional connection, and risk of overreliance, which constrain competence — related
- AI-assisted scientific writing assignments risk fabricated references and altered chemical meaning — possibly the same claim (merge candidate)
- In a small case study, students given ChatGPT feedback valued its immediacy and alternative analytical perspectives, but one distrusted its answers as 'often made up' for complex data analysis — related
- LLM responses that are incomplete or incorrect prompt students to question, verify and improve the information, fostering critical thinking through cycles of validation and correction — related
- ChatGPT fabricates references: the authors caught it inventing a non-existent 'Ribeiro and Vala, 2020' article with full bibliographic details — related
- ChatGPT sustains coherent, human-like multi-topic conversation and produces convincing creative content, as shown by the authors' own prompting test — related
- Most of the article's body text was generated by ChatGPT from author prompts, and all references ChatGPT inserted were fictional — related
- Leading LLMs hallucinated legal authorities in 58%-88% of tested cases while failing to recognise incorrect legal assumptions in prompts
- AI systems can produce biased or inaccurate diagnostic outputs, requiring critical teacher evaluation before integration — related
- Teacher-provided correct answers and pre-prompting reduce erroneous LLM output and hallucinations compared with open chatbot use — related
- LLM-generated lessons produced inauthentic references even when combined with RAG over verified academic papers — related
- Large language models can produce fluent but factually inaccurate or fabricated outputs (AI hallucination), posing risks for uncritical users in science education — a broader claim this one bears on
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
genai-hallucination-convincing-incorrect-explanations |
title |
Gen-AI hallucinates sources and values and produces convincing but incorrect explanations, risking student misinformation |
description |
Gen-AI hallucinates sources and values and produces convincing but incorrect explanations, risking student misinformation |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — ivory-2026, ivory-2026-2 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.