cs.AISep 18, 2026

Reducing Barriers to Academic Support: Evaluating a Course-Specific RAG System for Addressing Help-Seeking Disparities in Higher Education

Authors: Andy Gray, Jake Hobbs

Organizations: School of Design, Bath Spa University, Bath, UK

Abstract

Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutors, while others hesitate due to anxiety, fear of judgement, uncertainty about expectations, or low confidence in their understanding. This may be especially evident in computing education, where programming tasks are cumulative and cognitively demanding. Although students increasingly turn to general-purpose generative AI tools, these can produce responses that are inaccurate, insufficiently contextualised, or misaligned with module expectations. This study presents and evaluates Beacon, a course-specific Retrieval-Augmented Generation (RAG) system providing private, immediate, module-aligned academic support. Grounding responses in approved teaching materials, Beacon was designed to lower barriers to help-seeking while encouraging independent learning. Using a design-based research approach, Beacon was developed iteratively and evaluated via mixed methods, combining questionnaires and semi-structured interviews with students and staff at a Higher Education institution. Students described Beacon's responses as closely aligned with module content and more trustworthy than unrestricted generative AI tools, valuing its use of pseudocode and scaffolded explanations over direct solutions. Although participants remained cautious about trusting AI-generated responses without verification, they viewed the system as a valuable first point of support before consulting lecturers or official resources. The findings suggest that carefully designed course-specific AI systems may reduce barriers to academic support by occupying an intermediary space between independent study and formal support. Rather than replacing educators, educational AI may be most valuable when it broadens access to guidance while preserving the pedagogical role of lecturers.

Figures & tables

Explore similar work

CardsList
  1. AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI

    Mar 9, 2026Jérémy Barghorn, Anna Sotnikova, Sacha Friedli +1Mathematics EducationEducational QA

  2. AI Slop or AI-enhancement? Student perceptions of AI-generated media for an English for Academic Purposes course

    Apr 8, 2026David James Woo, Deliang Wang, Kai GuoAI in EducationGenerative AI in Education

  3. Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance

    Jun 11, 2026Ben Torsion, Jun ZhouMulti-Agent LLM SystemsLLM Guardrails