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

Studies student perceptions of AI-generated instructional videos in computing education. Finds students value personalization and rapid production but express concerns about accuracy and the loss of instructor presence. Identifies clear preferences for hybrid approaches where AI generates draft content that instructors review and refine.

Key Findings

In a descriptive post-test survey, 170 computing students at two U.S. institutions watched three 3-minute AI-generated videos on the Markdown markup language, created with the Knowlify tool, and then reported their perceptions of the videos and of AI-generated video use in education more broadly.

Students rated the specific AI-generated videos as high-quality, accurate, and usable, and nearly half were unable to determine that the videos were AI-generated.

Despite positive ratings of the videos themselves, students expressed limited comfort with the widespread adoption of AI-generated videos in the classroom.

Students 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.

The study positions AI video generation as a way for instructors to create personalized educational videos efficiently and cost-effectively, extending computing-education research beyond text-based AI tools.

Study Design & Method

The study addresses a gap in computing education research, which has focused largely on text-based AI tools for developing learning resources even as advances in AI video models make high-quality personalized videos feasible. Using a descriptive post-test survey design, the researchers recruited 170 computing students across two U.S. institutions. Participants watched three three-minute AI-generated videos on Markdown and completed a survey covering both perceptions of those videos and broader views on AI-generated videos in education. Outcomes were analyzed descriptively, with attention to whether students could detect AI authorship and how stated preferences varied by use case.

Implications for AI in Education

The results give computing instructors an evidence base for purposeful use: AI-generated video is acceptable for targeted, supplementary, visually oriented content, but students remain wary of it replacing instructor interaction or carrying high-stakes, accuracy-sensitive instruction. The fact that nearly half of students could not detect AI authorship, alongside concerns about inaccurate content, underscores the need for transparency and review workflows in which instructors validate AI-produced media. The preference for a hybrid model — AI-generated drafts refined by instructors — aligns with Instructional Design principles that treat generative tools as augmentations of, rather than replacements for, the instructor's role in CS Education, and it connects to broader questions of Student Experience and trust in Generative AI learning resources.

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Citation

Esse Ciego, Shubbhi Taneja, Wilson Wong, Amanpreet Kapoor (2026). Student Perceptions and Preferences Regarding AI-Generated Instructional Videos in Computing Education. arXiv:2607.28203. cs.HC.