Integrating natural language processing applications into instructional design: Benefits and challenges

Abstract

Abstract

The integration of natural language processing (NLP) into instructional design is examined by focusing on its potential to enhance personalized learning in distance education. The study is grounded in constructivism, explaining that learners construct knowledge through experiential learning and social interactions. A three-level model synthesizing educational psychology, biology, and computer science is adopted as a framework to explain how learners learn through interaction with NLP applications. Since the three-level model was recently introduced, this study validates it using Patterson’s (1986) criteria. Through plugins and APIs, NLP applications are integrated into learning management systems (LMSs) such as Moodle, Canvas, and D2L Brightspace, enabling real-time feedback, automated grading, and personalized support. Despite these advantages, challenges persist, including ethical concerns, data privacy issues, digital divides, and the risk of hallucination. To address these challenges, this paper proposes implementing AI governance frameworks, ensuring equitable access, and promoting AI literacy among learners and educators. These strategies aim to ensure that NLP is used ethically, inclusively, and effectively to support sustainable, meaningful learning outcomes.

Keywords: natural language processing (NLP), instructional design, artificial intelligence, a three-level model, learning management system