Optimizing AI-Based Video Summarization for Educational Media: A Comparative Evaluation of OpenAI, SumTube, and NoteGPT
DOI:
https://doi.org/10.31943/gw.v17i1.893Keywords:
AI Video Summarization, OpenAI, NoteGPT, SumTube, Mobile Learning, Distance EducationAbstract
Video content is increasingly central to distance and mobile learning, yet the volume of instructional media available to learners creates a pressing need for tools that can efficiently distill key content without compromising pedagogical integrity. This study comparatively evaluates three AI-based video summarization tools OpenAI, SumTube, and NoteGPT applied to authentic educational media from UT Radio Mobile at Universitas Terbuka. Eleven videos across two content categories were analyzed: promotional programs (Seputar UT) and module-based instructional content (Tutorial Radio). Using a comparative evaluative design, summary outputs were assessed against four integrated criteria accuracy, suitability, clarity, and processing time by expert validators, with inter-rater consistency reported across content categories. Results indicate that OpenAI achieved the highest accuracy for promotional content (93.3%) through narrative-preserving abstraction, while NoteGPT demonstrated stronger performance on instructional content (85.7%) but exhibited systematic semantic drift including terminological substitution, logical inversion, and topic conflation with meaningful consequences for novice learners. SumTube consistently delivered the fastest processing times (~4 minutes), making it suitable for time-constrained mobile learning contexts, though its extractive architecture rendered outputs susceptible to non-instructional content inclusion. Beyond tool benchmarking, the study demonstrates that summary quality has direct implications for cognitive load, knowledge retention, and learner engagement, and that prompt engineering functions as a form of pedagogical mediation that shapes the instructional alignment of AI-generated outputs. Findings support a hybrid, content-sensitive summarization model and offer theoretically grounded guidance for integrating AI summarization tools into distance and mobile education platforms.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Universitas Wiralodra

This work is licensed under a Creative Commons Attribution 4.0 International License.
The use of non-commercial articles will be governed by the Creative Commons Attribution license as currently approved at http://creativecommons.org/licenses/by/4.0/. This license allows users to (1) Share (copy and redistribute the material in any medium) or format; (2) Adapt (remix, transform, and build upon the material), for any purpose, even commercially.





