Can Consumer Chatbots Reason? A Student-Led Field Experiment Embedded in an "AI-for-All" Undergraduate Course
Abstract
Claims about whether large language model (LLM) chatbots "reason" are typically debated using curated benchmarks and laboratory-style evaluation protocols. This paper offers a complementary perspective: a student-led field experiment embedded as a midterm project in UNIV 182 (AI4All) at George Mason University, a Mason Core course designed for undergraduates across disciplines with no expected prior STEM exposure. Student teams designed their own reasoning tasks, ran them on widely used consumer chatbots representative of current capabilities, and evaluated both (i) answer correctness and (ii) the validity of the chatbot's stated reasoning (for example, cases where an answer is correct but the explanation is not, or vice versa). Across eight teams that reported standardized scores, students contributed 80 original reasoning prompts spanning six categories: pattern completion, transformation rules, spatial/visual reasoning, quantitative reasoning, relational/logic reasoning, and analogical reasoning. These prompts yielded 320 model responses plus follow-up explanations. Aggregating team-level results, OpenAI GPT-5 and Claude 4.5 achieved the highest mean answer accuracy (86.2% and 83.8%), followed by Grok 4 (82.5%) and Perplexity (73.1%); explanation validity showed a similar ordering (81.2%, 80.0%, 77.5%, 66.2%). Qualitatively, teams converged on a consistent error signature: strong performance on short, structured math and pattern items but reduced reliability on spatial/visual reasoning and multi-step transformations, with frequent "sound right but reason wrong" explanations. The assignment's primary contribution is pedagogical: it operationalizes AI literacy as experimental practice (prompt design, measurement, rater disagreement, and interpretability/grounding) while producing a reusable, student-generated corpus of reasoning probes grounded in authentic end-user interaction.
Cite
@article{arxiv.2601.04225,
title = {Can Consumer Chatbots Reason? A Student-Led Field Experiment Embedded in an "AI-for-All" Undergraduate Course},
author = {Amarda Shehu and Adonyas Ababu and Asma Akbary and Griffin Allen and Aroush Baig and Tereana Battle and Elias Beall and Christopher Byrom and Matt Dean and Kate Demarco and Ethan Douglass and Luis Granados and Layla Hantush and Andy Hay and Eleanor Hay and Caleb Jackson and Jaewon Jang and Carter Jones and Quanyang Li and Adrian Lopez and Logan Massimo and Garrett McMullin and Ariana Mendoza Maldonado and Eman Mirza and Hadiya Muddasar and Sara Nuwayhid and Brandon Pak and Ashley Petty and Dryden Rancourt and Lily Rodriguez and Corbin Rogers and Jacob Schiek and Taeseo Seok and Aarav Sethi and Giovanni Vitela and Winston Williams and Jagan Yetukuri},
journal= {arXiv preprint arXiv:2601.04225},
year = {2026}
}