Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.38029

Critical Thinking with Generative AI: A Constraint-First Design Pilot of a Thinking-Partner Intervention

Fatima Tuz Zahra·Jiangen He·David M. Bowers·Wei Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Generative AI (GenAI) tools entered higher education classrooms faster than the field was able to study their effects on learning. One concern is that GenAI may displace the critical thinking and AI literacy that students will need after graduation. This paper reports a Design-Based Research pilot of a GenAI-assisted critical thinking framework, in which ChatGPT was used as a thinking partner in an undergraduate research methods and statistics course during Spring 2025 (N = 14). The mixed-methods design combined pre- and post-intervention measures of statistical learning (AASCDM), AI literacy (MAILS), and critical thinking (WGCTA) with instructor field notes, student artifacts, and student-AI interaction logs. Pre-post tests showed gains on every AASCDM dimension and on eight of nine MAILS dimensions, while WGCTA percentiles did not change. Qualitative analysis identified four themes: the ways students positioned the LLM (as answer generator, validator, or co-thinker); the depth of student engagement (procedural vs. conceptual); occasional humanizing of the tool; and the role of curriculum design in shaping each of the prior three. Read together, the findings indicate that one semester of GenAI-assisted instruction can move domain learning and self-reported AI literacy but does not move standardized critical thinking, and that the modal student-LLM relationship is one of validation instead of dialogue. We end with design principles for the next iteration of the framework and implications for research on adaptive and personalized learning.

Explore related subjects

cs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fatima Tuz Zahra, Jiangen He, David M. Bowers, Wei Wang. 2026-09-29. Critical Thinking with Generative AI: A Constraint-First Design Pilot of a Thinking-Partner Intervention. https://arxiv.org/abs/2609.38029

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Beyond Judgment: Exploring Large Language Models as Non-Judgmental Support for Maternal Mental Health

In the age of Large Language Models (LLMs), much work has already been done on how LLMs support medication advice and serve as information providers; however, how mothers use these tools for emotional and informational support to avoid social judgment remains underexplored. This study conducted a 10-day mixed-methods exploratory survey ($N=107$) to investigate how mothers use LLMs as a non-judgmental resource for emotional support and regulation, and for situational reassurance. Our findings show that mothers are asking LLMs various questions about childcare to reassure themselves and avoid judgment, particularly around childcare decisions, maternal guilt, and late-night caregiving. Open-ended responses also show that mothers are comfortable with LLMs because they do not have to think about social consequences or judgment. Although mothers use LLMs for quick information or reassurance to avoid judgment, over half of the participants value human warmth more than LLMs; however, a significant minority, especially those in joint families, consider LLMs to avoid human judgment. These findings help understand how LLMs can be framed as low-risk interaction support rather than a replacement for human support, and highlight the role of social context in shaping emotional technology use.

cs.HC↗
arXiv2026

Avoiding Social Judgment, Seeking Privacy: Investigating why Mothers Shift from Facebook Groups to Large Language Models

Social media platforms, especially Facebook parenting groups, have long been used as informal support networks for mothers seeking advice and reassurance. However, growing concerns about social judgment, privacy exposure, and unreliable information are changing how mothers seek help. This exploratory mixed-method study examines why mothers are moving from Facebook parenting groups to large language models such as ChatGPT and Gemini. We conducted a cross-sectional online survey of 109 mothers. Results show that 41.3% of participants avoided Facebook parenting groups because they expected judgment from others. This difference was statistically significant across location and family structure. Mothers living in their home country and those in joint families were more likely to avoid Facebook groups. Qualitative findings revealed three themes: social judgment and exposure, LLMs as safe and private spaces, and quick and structured support. Participants described LLMs as immediate, emotionally safe, and reliable alternatives that reduce social risk when asking for help. Rather than replacing human support, LLMs appear to fill emotional and practical gaps within existing support systems. These findings show a change in maternal digital support and highlight the need to design LLM systems that support both information and emotional safety.

cs.HC↗
arXiv2026

Learning to Assign Prediction Tasks to Agents with Capacity Constraints

We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models. We focus on sequential learning of agent expertise and assignment policies where each agent is constrained to handle a fraction of tasks. We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context. We then develop a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall performance. Experimental results over a variety of tabular, image, and text prediction tasks demonstrate systematic gains from our policy-learning algorithms relative to non-contextual baselines across different types of agents, including LLMs and humans.

cs.HC↗