AUTOMATED ESSAY EVALUATION SYSTEMS FOR SCIENTIFIC WRITING IN ENGINEERING EDUCATION

Despite the importance of scientific writing for engineering students, the technical discourse poses a significant challenge for learners. The promise of Automated Essay Evaluation (AEE) Systems to provide timely and reliable feedback has enabled greater development of writing instruction through expedited and automated support. This paper examines the development of AEE systems designed specifically for scientific writing in the context of engineering education. We review existing research that applies Natural Language Processing (NLP), machine learning, and even rule-based linguistics to evaluate structural coherence, domain vocabulary, and argumentative discourse. Additionally, we evaluate the pedagogical advantages and drawbacks of AEE concerning fostering self-regulated learning, instructor support, and assessment rigor. Results from pilot case studies and experiments illustrate increased student-writing performance and engagement with the task. This research emphasizes the need for domain-specific adaptation of AEE systems and algorithmic transparency, automated ethics, and machine-logic debates, challenging the predisposed notions of the use of AEE in engineering writing instruction.

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