Research Article

Knowledge visualization to improve writing performance in undergraduate engineering courses

Donggil Song 1 * , Anne Lippert 2
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1 Texas A&M University, College Station, TX, USA2 Prairie View A&M University, Prairie View, TX, USA* Corresponding Author
Journal of Digital Educational Technology, 6(1), April 2026, ep2606, https://doi.org/10.30935/jdet/17551
Submitted: 17 March 2025, Published: 15 December 2025
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ABSTRACT

Over-reliance on generative artificial intelligence (AI) for writing tasks can have negative effects on engineering education. Despite growing concerns over generative AI’s impact on authentic writing skills, limited research has critically examined alternatives that integrate between self-regulated learning (SRL) theories with knowledge visualization tools to foster monitoring and evaluation processes in engineering education. This exploratory study addresses this gap by exploring how machine learning-based text analytics can scaffold SRL, extending prior frameworks on information processing. Thirty participants were recruited from two sections of an engineering technology course. As a course task, participants wrote essays using the knowledge visualization system over a semester. SRL skills were measured through a survey, and final course grades served as a measurement of learning performance (LP). First, there were no noticeable relationships between students’ SRL, LP, and writing performance (WP). Second, regression analysis showed that SRL and course grades do not significantly predict WP. Third, engineering students’ WP significantly increased over time. Lastly, there were no differences in WP changes over time between high and low SRL or LP groups. However, there is a main effect of LP on WP, and an interaction effect of LP and time was observed in the evaluating component.
Keywords: engineering education, writing performance, generative AI, knowledge visualization, self-regulated learning

CITATION (APA)

Song, D., & Lippert, A. (2026). Knowledge visualization to improve writing performance in undergraduate engineering courses. Journal of Digital Educational Technology, 6(1), ep2606. https://doi.org/10.30935/jdet/17551

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