cs.HCMar 10, 2026

Beyond Passive Viewing: A Pilot Study of a Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

Authors: Mohammed Abraar, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat

Organizations: Vizuara AI Labs

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Abstract

The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critical pedagogical shortcomings. Traditional video-based instruction, while cost-effective and scalable, demonstrates systematic failures in both sustaining learner engagement and facilitating the deep conceptual mastery essential for AI literacy. We present a pilot study evaluating a novel hybrid learning platform that integrates real-time conversational AI tutors with traditional video lectures. Our controlled experiment (N = 58, mean age M = 21.4, SD = 2.8) compared traditional video-based instruction with our AI-augmented video platform. This study employed a sequential within-subjects design where all participants first completed the traditional video condition followed by the AI-augmented condition, providing direct comparisons of learning outcomes. We measured learning effectiveness through immediate post-tests and delayed retention assessments (2-week delay). Results suggest improvements in learning performance: immediate post-test performance showed a large effect size (d = 1.505) with participants scoring 8.3 points higher after AI-augmented instruction (91.8 vs 83.5 out of 100, p < .001). Behavioral analytics revealed increased engagement duration (71.1% improvement with AI tutoring) in the experimental group. This pilot study provides preliminary evidence that conversational AI tutors may enhance traditional educational delivery, suggesting a potential avenue for developing scalable, adaptive learning systems.

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Figure 1: The AI Augmented Learning Platform interface demonstrating the experimental setup for Condition B. The platform integrates a video player (showing Byte Pair Encoding topic) with an interactive tutor chat system, character selection features, and voice interaction capabilities. The tutor provides real-time guidance and welcomes learners to engage actively with the content.
Figure 2: Experimental procedure flow diagram showing the sequential within-subjects design used in this study. All participants (N=58) progressed through sequential conditions: Condition A (Traditional Video) with Immediate Pre-test, Condition B (AI-Augmented) with Immediate Post-test, brief intermission, and Delayed Retention Assessment. This design enabled direct within-subjects comparisons of learning outcomes.
Figure 3: Learning performance comparison across assessment types showing significant improvement from pre-test to post-test ( p<.001p<.001 ) with maintained performance in retention testing. Scores are out of 100 points. Error bars represent standard deviation.
Figure 4: Individual learning trajectories across all participants (N=58) showing varied patterns of improvement. The red line represents the mean trajectory, demonstrating overall learning gains from pre-test to post-test with slight decline in retention testing. Scores are out of 100 points.
AssessmentCondition BCondition At(57)p-valueCohen’s d
 (AI-Augmented)(Traditional Video)   
Immediate Post-test91.8 ±\pm 5.383.5 ±\pm 5.9-11.462p<.001p<.0011.505
Delayed Retention Assessment90.5 ±\pm 5.6N/AN/AN/AN/A
Table 1: Comparison of Learning Outcomes. Condition B = AI-Augmented, Condition A = Traditional Video. Scores out of 100. Paired-samples t-tests (N=58).
Figure 5: Engagement duration analysis comparing traditional video-based learning (pre-test) with AI-augmented learning (post-test). Left panel shows box plot comparison with mean values. Right panel shows density distribution revealing significantly increased engagement with AI tutoring.
MetricCondition ACondition B
 (Traditional Video)(AI-Augmented)
Avg Session31.8 ±\pm 10.7 min54.5 ±\pm 19.2 min
Duration  
ChatN/A23.4 ±\pm 6.8 msgs
Interactions  
ProactiveN/A8.7 ±\pm 2.1 resp
Table 2: Behavioral Analytics of Learner Engagement. Condition A = Traditional Video, Condition B = AI-Augmented. Values show mean ±\pm SD.
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