Evidence on GenAI in chemistry education is concentrated in short-term outcomes, with durable transfer and retention rarely tested¶

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? The reviewed literature shows stronger evidence for short-term performance, perceptions, products, and feasibility than for delayed transfer or durable conceptual change. → Vega-Baudrit JR and Rivera Álvarez A 2026

Evidence¶

Vega-Baudrit JR and Rivera Álvarez A 2026¶

Vega-Baudrit JR and Rivera Álvarez A (2026) Generative artificial intelligence in university chemistry education: a critical review using Johnstone’s chemistry triplet and Biggs’ 3P model. Front. Educ. 11:1918707. https://doi.org/10.3389/feduc.2026.1918707

q2 · i? · review · r2

The review's synthesis across its representative evidence matrix, which also states that comparative and longitudinal designs remain limited and that outcome measures often emphasize perception or immediate product quality. No pooled effect estimate was computed.

"The evidence considered here is stronger for short-term performance, perceptions, products, and feasibility than for delayed transfer or durable conceptual change."

Discussion¶

Related Claims¶

• Product-outcome studies report GenAI-related gains on product-quality measures but do not assess durable, transferable learning — a narrower finding that bears on this claim
• The Global South GenAI literature shows limited geographic representation, stakeholder imbalance, and insufficient exploration of long-term outcomes — related
• As cited second-hand in an AI-literacy framework paper: ChatGPT use improved short-term essay scores but triggered metacognitive laziness with no significant knowledge-transfer gains — a narrower finding that bears on this claim
• Falsifiable hypothesis: unrestricted AI use on deep-processing tasks will produce a product–process dissociation—higher-rated assignments but lower unaided delayed transfer — related
• The decisive empirical test of AI-augmented instruction frameworks is whether students retain representational competence when AI support is withdrawn — related
• Immediate task success does not establish learning that persists without AI support — possibly the same claim (merge candidate)
• Agentivism generates six empirically testable propositions linking delegation structure, verification, reconstruction, and reduced-support transfer to learning outcomes — related
• AI-assisted task performance can rise without corresponding gains in underlying knowledge, metacognitive processing, or independent reasoning — related
• Short-term, non-longitudinal training programmes pose challenges for estimating long-term skill retention — related