LLMs in text linguistics teaching: An exploratory study with genAI novices in higher education

Synthesis: Brocca and Garassino (2026) use an action research design to examine how generative-AI novices in higher education design prompts and evaluate LLM outputs in text linguistics teaching. Students' reports show novices refine prompts through trial and error, occasionally use in-context examples, or simplify complex instructions; end-of-term reflections reveal limited prompting competence but growing confidence in applying subject-specific knowledge. Students often attribute unsatisfactory results to the LLM rather than their own prompt formulation, and challenges such as anthropomorphising the models and overgeneralising limited outcomes emerge. The study finds that students engage in metacognitive reflection with LLMs chiefly when disciplinary knowledge is well consolidated, and concludes that limited prompt-design knowledge remains a major obstacle — signalling the need for explicit Prompt Engineering instruction in disciplinary AI use.

Key Findings

Novice prompting behaviour. genAI novices refine prompts through trial and error and occasionally use in-context examples or simplify complex instructions — a natural but limited progression without structured guidance.

Limited prompting competence. End-of-term reflections indicate limited prompting competence despite growing confidence in applying subject-specific knowledge, exposing a gap between perceived and actual AI skill.

Attribution bias. Students often attributed unsatisfactory results to the LLM's limitations rather than to their own prompt formulation, a form of misattribution relevant to Trust Calibration and AI Literacy.

Metacognition and disciplinary grounding. Students reported metacognitive reflection with LLMs particularly when disciplinary knowledge was already well consolidated, suggesting Metacognition in AI use depends on domain foundations.

Pedagogical implication. Limited knowledge of prompt design is a major obstacle; the authors argue for explicit prompt-engineering instruction within disciplinary teaching to help novices harness LLM potential in text analysis.

Connected Concepts

• LLM
• Language Learning
• Prompt Engineering
• Metacognition
• Discipline Specific AIED
• AI Literacy

Connected Articles

• LLM Reasoning Traces Metacognition — LLM reasoning traces and metacognition
• Teacher Authored Prompts Student AI Dialogue — Teacher-authored prompts for student-AI dialogue
• Learning To Prompt Adaptive Tutoring — Learning to prompt in adaptive tutoring
• Prompt Coach Agentic Tutor Prompt Engineering — Prompt coach: agentic tutor for prompt engineering

Citation

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