The Explanation Deficit: How NLP Tools Correct Language Without Building Understanding

This paper, “The Explanation Deficit: How NLP Tools Correct Language Without Building Understanding,” by Jigme Thinley and Arun Udayasuriyan of Parul University in India, investigates a fundamental paradox in contemporary language learning technology —the “efficacy gap.” The paper challenges the hype toward Natural Language Processing (NLP)-based tools for surface error detection and correction of grammar, spelling, and pronunciation to claim that despite strides made toward technological feasibility in these more superficial areas of language learning, there is little translation to effective communication in face-to-face interactions or even written dialogue beyond either medium. The authors propose a new “Three-Gap Analytical Framework” identifying the cognitive gap (tools provide correct answers but no understanding), the contextual gap (tools score language in a bubble without recognition of social and cultural nuances), and the motivational gap (gamification prizes compliance/streak-maintenance over real interest/engagement). The paper uses comparative case analyses of tools including Grammarly, AI chatbots, and vocabulary apps to show that while learners may gain from them in terms of superficial kinks — 34% fewer grammar errors here, or 22% faster speaking rates there — they rarely develop deeper competencies such as argument quality, pragmatic appropriateness or conversational management.