arXiv subjects

Amanpreet Kapoor

Publications and source records attributed to Amanpreet Kapoor.

3 recordsLinked to original sources

arXiv2026

Guardrails or Roadblocks? Effects of Pedagogical Style and Context Awareness in AI Teaching Assistants for Programming

AI teaching assistants (AI TAs) backed by large language models (LLMs) and pedagogical guardrails are increasingly being integrated into programming courses, providing students with scalable access to hints, conceptual explanations, and code-level feedback. However, guardrails may also create friction. If students feel that the support provided is overly restrictive or poorly contextualized to their current progress, they may bypass approved tools for general-purpose LLMs. To investigate how AI TA design affects students' learning experiences, we conducted a randomized controlled trial with 132 students in an introductory programming course. Students completed three tasks related to code-writing and debugging and were randomly assigned to one of four AI TAs varied across two dimensions: pedagogical guidance style (Socratic vs. Direct instruction) and context awareness (no context vs. full context of the problem and student solution). We examined students' perceptions, interaction behaviors, and evidence of post-task comprehension. Students rated the Socratic AI TA with full context least favorably, reporting significantly lower perceived support for task completion. Descriptively, this condition also showed the highest observed interaction stress, the highest rate of external LLM use, and the lowest proportion of post-task explanations demonstrating full comprehension, though these differences were not statistically significant. These findings suggest that guardrailed AI TAs are not automatically better for learning. Instead, their effectiveness depends on how pedagogical guidance and contextual awareness are balanced in ways that students experience as useful, supportive, and worth continuing to use.

cs.HC↗

arXiv2026

Student Perceptions and Preferences Regarding AI-Generated Instructional Videos in Computing Education

Students differ in how they prefer to engage with learning resources, with some favoring textual materials and others visual or video-based content. Recent advances in generative AI have led CS education research to focus on text-based AI tools for developing learning resources. However, advances in AI video models and the rapid proliferation of AI video generation tools have made it possible for instructors to create high-quality personalized educational videos efficiently and cost-effectively. Understanding students' perceptions of AI-generated videos is thus critical for helping CS instructors know when and how to use them purposefully. To address this gap, we conducted a descriptive post-test survey study in which 170 computing students at two U.S. institutions watched three 3-minute AI videos on the Markdown markup language created with Knowlify. Students then completed a survey about their perceptions of the Markdown videos and their broader views on the use of AI-generated videos in education. Students rated the Markdown videos as high-quality, accurate, and usable, with nearly half unable to determine whether the videos were AI-generated. At the same time, students expressed limited comfort with the widespread adoption of AI videos in the classroom. They preferred AI videos for simple, supplemental, and visual use cases, while expressing concerns about lower-quality or inaccurate content, reduced instructor interaction, and diminished educational value.

cs.HC↗

arXiv2025

Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails

AI-powered chatbots and digital teaching assistants (AI TAs) are gaining popularity in programming education, offering students timely and personalized feedback. Despite their potential benefits, concerns about student over-reliance and academic misconduct have prompted the introduction of "guardrails" into AI TAs - features that provide scaffolded support rather than direct solutions. However, overly restrictive guardrails may lead students to bypass these tools and use unconstrained AI models, where interactions are not observable, thus limiting our understanding of students' help-seeking behaviors. To investigate this, we designed and deployed a novel AI TA tool with optional guardrails in one lab of a large introductory programming course. As students completed three code writing and debugging tasks, they had the option to receive guardrailed help or use a "See Solution" feature which disabled the guardrails and generated a verbatim response from the underlying model. We investigate students' motivations and use of this feature and examine the association between usage and their course performance. We found that 50% of the 885 students used the "See Solution" feature for at least one problem and 14% used it for all three problems. Additionally, low-performing students were more likely to use this feature and use it close to the deadline as they started assignments later. The predominant factors that motivated students to disable the guardrails were assistance in solving problems, time pressure, lack of self-regulation, and curiosity. Our work provides insights into students' solution-seeking motivations and behaviors, which has implications for the design of AI TAs that balance pedagogical goals with student preferences.

cs.HC↗