A Discipline-Agnostic AI Literacy Course for Academic Research: Architecture, Pedagogy, and Implementation

TL;DR

This paper presents a discipline-agnostic AI literacy course designed to develop rigorous research skills in AI-assisted literature review, emphasizing verification, attribution, and responsible AI use, with positive self-reported confidence gains.

Contribution

It introduces a novel, scalable curriculum architecture for AI research literacy that fills a gap between technical AI courses and brief literacy interventions.

Findings

Significant confidence improvements in hallucination detection (d=+1.45)

Enhanced responsible AI use confidence (d=+1.33)

Improved AI attribution practice confidence (d=+2.40)

Abstract

The rapid integration of generative AI into academic workflows demands curricula that equip students not only with tool proficiency but with the critical judgment to use those tools responsibly in scholarly work. Existing offerings cluster around two inadequate poles: technical AI development courses serving narrow specialist audiences, and brief general-literacy interventions that cannot develop the sustained, practice-based competencies rigorous research requires. This paper reports the design, theoretical rationale, and implementation of BSTA 495/395: Getting Started with AI-Assisted Research, developed and delivered at Lehigh University (Spring 2026). The course addresses an underserved gap: the competencies required for rigorous AI-assisted literature review. Its architecture organizes instruction into four sequential modules aligned with the cognitive demands of that task: comprehension of individual papers, construction and validation of knowledge taxonomies, identification of research gaps, and synthesis and production of complete literature reviews. Each module embeds an explicit verification discipline and standardized AI attribution practice. Prerequisite-free and discipline-agnostic, the course enrolls upper-level undergraduates and graduate students across all fields with differentiated assessment expectations. Pre- and post-course survey data from the inaugural offering indicate substantial self-reported confidence gains, with the largest in hallucination detection (d = +1.45), responsible AI use (d = +1.33), and AI attribution practice (d = +2.40), consistent with the course's design emphasis. The course constitutes a replicable model for the emerging genre of AI research literacy curricula.