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Research Article

Efficiency vs. Effectiveness: Self-Regulated Learning with LLM-Mediated Help-Seeking

created 2026-08-27 · updated 2026-08-27 · self regulated learning, help seeking, llm, higher ed, stem education, qualitative research

Efficiency vs. effectiveness — a qualitative study of 20 STEM university students in Sweden showing that LLM chatbots are woven into a layered, context-dependent Help Seeking ecosystem rather than replacing human support. Students follow a four-stage process — deciding whether help is needed, choosing whom to ask, determining the type of help, and judging the help received — favoring instrumental over executive help-seeking to retain control of problem-solving.

Key Findings

  • A layered help-seeking ecosystem. LLMs do not displace peers and instructors; they become a low-barrier first step (e.g., "first ChatGPT, then classmates, and lastly teachers"). Peers remain important for conceptual negotiation and affective support, and instructors for complex, high-stakes issues.
  • Four-stage LLM-mediated help-seeking process (adapted from Karabenick & Berger's model): (1) deciding whether help is needed — students try tasks independently first to preserve learning value; (2) choosing a source — ChatGPT as immediate first step, then peers, then instructors; (3) determining the type of help — hints, explanations, Scaffolding, routine-work streamlining, or extending learning; (4) judging the help — exercising selective trust, verifying AI outputs against coursework or with humans.
  • Instrumental over executive help-seeking. Students favored instrumental help-seeking (hints, step-by-step guidance, concept explanations) over executive help-seeking (direct solutions), deliberately using ChatGPT as "a hint, an assisting tool, but not the standalone solution."
  • Selective trust and verification. Students verify LLM outputs with peers, course materials, or instructors when accuracy is uncertain — e.g., "I only go to the TA if we can't tell whether ChatGPT is making things up."

Implications

  • Adapt help-seeking models for LLM-mediated learning. The four-stage process provides a framework for understanding and measuring SRL-for-LLM help-seeking; the paper proposes draft survey items.
  • Support verification practices and instrumental help-seeking. Prolonged exposure to "good enough" AI outputs may normalize shortcuts at the expense of deeper learning, especially among low-SRL students — so students need targeted training on SRL strategies in LLM-mediated settings.
  • Distinguish Cognitive Offloading from efficiency. Reliance on LLMs for debugging or cross-language programming risks bypassing opportunities for independent problem-solving, even when students avoid direct-answer use.

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Citation

Viberg, O., Feldman-Maggor, Y., & Wong, J. (2026). Efficiency vs. effectiveness: Self-regulated learning with LLM-mediated help-seeking. Learning Letters, 8, Article 60. https://doi.org/10.20851/ll.v8.60

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