Comparing their own analyses with LLM outputs empowered students' sense of competence and reduced concerns about LLMs replacing human expertise¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Students perceived themselves as more capable than the LLM after comparing their own analyses with LLM-generated analyses. → Nicola Brocca 2026

Evidence¶

Nicola Brocca 2026¶

Nicola Brocca, Davide Garassino. (2026). LLMs in text linguistics teaching: an exploratory study with genAI novices in higher education. Computers and Education Open 11 (2026) 100414. https://doi.org/10.1016/j.caeo.2026.100414

q2 · i? · qualitative · r3

Authors' interpretation of the reflection data (Dataset 2): the activity's design, which followed prior manual analysis, led students to "perceived themselves to be more capable than the LLM". Some reflections also indicated reduced concern about LLMs replacing human expertise.

"By comparing their own analyses with the LLM-generated analyses, the activity empowered their sense of competence, as they perceived themselves to be more capable than the LLM."

Discussion¶

Learner Variables¶

• Self-Efficacy — outcome: instruction changes it

Related Claims¶

• Most students reported improved output-evaluation competence after comparing their own text analysis with LLM-generated analyses — related
• AI methodologies may redistribute epistemic agency, moving researchers from primary analyst to interpreter and validator of AI-generated outputs — related
• Students report negative experiences with ChatGPT including inaccurate outputs, lack of emotional connection, and risk of overreliance, which constrain competence — related
• Students perceived the teacher’s modeling with contrasting cases as a turning point that repositioned them as active gatekeepers of AI-generated content — related