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📄 Research Article

Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis

● high · 2026-07-10llm, higher-ed, student-experience, learning-analytics, personalized-learning, self-regulated-learning, generative-ai, scaffolding, ai-literacy

Presents a large-scale descriptive analysis of an AI learning assistant (Syntea) using objective log data from 77,543 higher-education students, characterizing real usage patterns, adoption, and engagement at scale. The work connects to broader debates about how Generative AI systems reshape Student Experience and the conditions under which AI support scaffolds rather than undermines learning. It has direct implications for Pedagogy AI Mistakes and the risk of Over Reliance when assistants absorb too much of the cognitive load. Findings also bear on AI Literacy and Self Regulated Learning, and on how institutions should govern Student Experience and Academic Integrity. Practitioners in Higher Ed and teachers can use the evidence to calibrate when to deploy LLM-based help and how to pair it with feedback that preserves learning gains.

Connected Concepts

  • Generative AI
  • Student Experience
  • Scaffolding
  • Over Reliance
  • AI Literacy
  • Self Regulated Learning
  • Academic Integrity
  • Higher Ed
  • Teacher Role
  • LLM
  • Feedback Loop

    • Connected Articles

  • Pedagogy AI Mistakes

    • Citation

    A. Leypold, M. Knolle, A. F. D. S. et al. (2026). Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis. arXiv:2607.08748.