Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.00338

Three Pathways of Student-AI Interaction: Constraint-First Design for Higher-Order Thinking

Fatima T. Zahra
View original source ↗Read source document ↗Export metadata ↓

Abstract

How students interact with artificial intelligence (AI) systems in educational settings may determine whether that interaction supports or displaces critical thinking. This paper introduces two contributions. The first is the Three Paths of Student-AI Interaction, a typological framework identifying three qualitatively distinct modes of student-AI engagement: Passive Review, Direct Question, and Strategic Dialogue. The second is the Next Level Teaching Blueprint (NLTB), a three-stage instructional design system intended to make Strategic Dialogue more likely. Qualitative content analysis of 50 randomly sampled student-AI interaction messages from an undergraduate research methods course was used to examine the typology. Two human coders achieved 68% path-level agreement ($κ$ = .48), with 80% agreement on Strategic Dialogue identification specifically. GPT-5, used as a third coder, produced a similar overall distribution and introduced a coding category absent from the human scheme. Path 1 (Passive Review) accounted for 46% of exchanges in the primary researcher's classifications, Path 2 (Direct Question) for 18%, and Path 3 (Strategic Dialogue) for 36%. A second, descriptively examined dataset contained predominantly Strategic Dialogue content, offering a preliminary indication that instructional framing may influence which path students take. Together, the Three Paths framework and the NLTB contribute a language for describing student-AI interaction and a design approach for supporting higher-order engagement.

Explore related subjects

cs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fatima T. Zahra. 2026-09-29. Three Pathways of Student-AI Interaction: Constraint-First Design for Higher-Order Thinking. https://arxiv.org/abs/2610.00338

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

KEEP EXPLORING

Related papers

arXiv2026

AI Behavioral Science: A Framework and Agenda

We discuss the challenges and opportunities present in the rapidly emerging area of ``AI Behavioral Science.'' We frame it via three subfields. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop models of AI and methods for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to model, assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, predicting, and analyzing human behaviors. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to analyze and model human-AI interactions including how human and AI behaviors depend on interactions at the individual level, how interacting systems of humans and AI behave, and ultimately how AI's integration into society affects economic and political outcomes. We discuss current research, questions, agendas, and goals in each of these three subfields and how they depend upon each other.

cs.HC↗
arXiv2026

Scaling Peer Assessments: An Integrity Report from a Large Engineering Internship

Assessing learning in large classrooms presents a significant challenge for individual instructors, who may have limited capacity to evaluate the understanding, participation, and assessment behaviour of every student. Peer assessments have been a way of distributing this responsibility among learners, allowing them to evaluate and provide feedback to one another while reducing dependence on instructor-led assessments. Building on this approach, we implemented a peer validation model within a large, multi-institutional internship programme in which students who demonstrated sufficient understanding were authorised to assess and validate their peers through short oral discussions. The assessment process began with the instructor validating a small group of students, who were then authorised to validate their peers, allowing the process to gradually expand across the cohort and operate at scale. This study examines how participants experienced the model and the extent to which assessment integrity was maintained, using an end-of-programme survey of 238 consenting respondents. Most participants regarded the activity as worthwhile, with 79.8% reporting that they solved problems they could not previously solve. However, 29.0% acknowledged at least one instance of reduced effort, a lowered validation standard, or reciprocal validation, while 88.7% believed that at least a little validation had occurred without proper examination. When asked how the process could be strengthened, participants selected post-validation discussion of solutions approximately twice as often as closer auditing or mentor-led validation. These findings provide descriptive evidence of both the potential and the integrity challenges of using peer validation as a scalable assessment approach in large learning environments.

cs.HC↗
arXiv2026

Who Thinks First? Designing Productive Friction with Engage-to-Unlock GenAI

Generative AI can support writing, but frictionless access may cause cognitive offloading before users develop their own ideas. We introduce Engage-to-Unlock, a productive-friction mechanism that unlocks generative capabilities after users meaningfully engage with the task. In a controlled experiment (N = 398), participants completed a writing task under one of four conditions: Human-Only, Standard Chatbot, Engage-to-Unlock, or Time-Matched Unlock, which matched unlock timing to Engage-to-Unlock participants but independent of users' engagement, then evaluated passages for evidence and inferential errors. Results show that Engage-to-Unlock redistributed effort across tasks: participants spent more time writing and less time evaluating, without increasing overall task duration. They also submitted more prompts than in other AI-assisted conditions and showed the highest accuracy-per-time evaluation efficiency across conditions. These findings suggest that designing GenAI access to encourage early human engagement may provide a productive form of friction, while retaining active AI use and efficient downstream evaluation.

cs.HC↗