arXiv subjects

Caitlin Morris

Publications and source records attributed to Caitlin Morris.

4 recordsLinked to original sources

arXiv2026

InquiryBits: Sharing AI Conversation Traces to Support Collaboration Within Trust Boundaries

AI chat tools are shifting problem-solving and brainstorming conversations away from colleagues and into private AI interactions, reducing the shared awareness that supports team coordination. We introduce InquiryBits, a system that shares minimal summaries of AI conversations within configurable trust boundaries, separating AI-only analysis from human-visible sharing. In a study with 80 professionals, we find that people are broadly willing to share these traces to support collaboration and avoid duplicating work - but only within bounded groups. Comfort drops sharply as audience expands beyond close teams; the level of detail shared matters less than who can see it, with a preference for more detail over less within trusted groups. These findings suggest that trust boundaries, more than information granularity, may be the most impactful design parameter.

cs.HC↗

arXiv2026

Same Feedback, Different Source: How AI vs. Human Feedback Attribution and Credibility Shape Learner Behavior in Computing Education

As AI systems increasingly take on instructional roles - providing feedback, guiding practice, evaluating work - a fundamental question emerges: does it matter to learners who they believe is on the other side? We investigated this using a three-condition experiment (N=148) in which participants completed a creative coding tutorial and received feedback generated by the same large language model, attributed to either an AI system (with instant or delayed delivery) or a human teaching assistant (with matched delayed delivery). This three-condition design separates the effect of source attribution from the confound of delivery timing, which prior studies have not controlled. Source attribution and timing had distinct effects on different outcomes: participants who believed the human attribution spent more time on task than those receiving equivalently timed AI-attributed feedback (d=0.61, p=.013, uncorrected), while the delivery delay independently increased output complexity without affecting time measures. An exploratory analysis revealed that 46% of participants in the human-attributed condition did not believe the attribution, and these participants showed worse outcomes than those receiving transparent AI feedback (code complexity d=0.77, p=.003; time on task d=0.70, p=.007). These findings suggest that believed human presence may carry motivational value, but that this value depends on credibility. For computing educators, transparent AI attribution may be the lower-risk default in contexts where human attribution would not be credible.

cs.HC↗

arXiv2026

Same Feedback, Different Source: How AI vs. Human Feedback Shapes Learner Engagement

When learners receive feedback, what they believe about its source may shape how they engage with it. As AI is used alongside human instructors, understanding these attribution effects is essential for designing effective hybrid AI-human educational systems. We designed a creative coding interface that isolates source attribution while controlling for content: all participants receive identical LLM-generated feedback, but half see it attributed to AI and half to a human teaching assistant (TA). We found two key results. First, perceived feedback source affected engagement: learners in the TA condition spent significantly more time and effort (d = 0.88-1.56) despite receiving identical feedback. Second, perceptions differed: AI-attributed feedback ratings were predicted by prior trust in AI (r = 0.85), while TA-attributed ratings were predicted by perceived genuineness (r = 0.65). These findings suggest that feedback source shapes both engagement and evaluation, with implications for hybrid educational system design.

cs.HC↗

arXiv2026

When Peers Outperform AI (and When They Don't): Interaction Quality Over Modality

As AI increasingly enters the classroom, what changes when students collaborate with algorithms instead of peers? We analyzed 36 undergraduate students learning graph theory through peer collaboration (n=24) or AI assistance (n=12), using discourse analysis to identify interaction patterns shaping learning outcomes. Results reveal a collaboration quality divide: high-quality peer interactions generated curiosity and engagement that AI couldn't match, yet low-quality peer interactions performed worse than AI across dimensions. AI showed a paradoxical pattern, building confidence in knowledge while reducing curiosity and deeper engagement. Interaction quality emerged from dynamic patterns rather than individual traits, with early discourse markers predicting outcomes. Students treated AI as a transactional information source despite its collaborative design, revealing fundamental differences in human versus algorithmic engagement. Our findings suggest AI in education need not replace peer learning but can recognize struggle and support both peer and AI interactions toward productive learning experiences.

cs.HC↗