Journal of Pioneering Artificial Intelligence Research

Abstract

Generative artificial intelligence (GenAI) is increasingly influencing how students search for, interpret, and communicate historical information. While GenAI can provide rapid access to historical explanations and generate diverse forms of content, its outputs may contain inaccuracies, fabricated references, decontextualized claims, bias, and excessive certainty. These characteristics create an important challenge for history education, where students are expected to evaluate evidence, identify authorship and provenance, contextualize historical claims, corroborate sources, and construct evidence-based judgments. This study examines the relationship between students' engagement with generative AI and their historical source-criticism competence in higher education. Using a quantitative descriptive-correlational design, the study proposes a sample of 200 university students enrolled in general education and history-related courses at a Philippine state university. Data are gathered through a researcher-developed instrument measuring GenAI engagement, awareness of AI limitations, and historical source-criticism competence in terms of sourcing, contextualization, corroboration, evidence evaluation, and historical judgment. Descriptive statistics, Pearson's product-moment correlation, and multiple regression are proposed for analysis. The illustrative results suggest that students may demonstrate moderate-to-high GenAI engagement but only moderate competence in independently verifying AI-generated historical claims. The expected contribution of the study is an empirically grounded framework for strengthening source criticism and historical thinking in an AI-mediated learning environment.