Abstract
This chapter examines the integration of artificial intelligence (AI) in language learning assessment and feedback. It highlights limitations of traditional models before exploring AI-driven innovations in automated scoring, speech recognition, multimodal analytics and adaptive testing. The ensuing pedagogical transformation is discussed. AI feedback mechanisms including automated writing evaluation, intelligent tutoring systems, conversational agents, affective computing and learning analytics dashboards are analyzed. Benefits are presented alongside challenges regarding ethics, overreliance on technology and transparency. Case studies provide examples across educational contexts. The future outlook considers emerging innovations, increased accessibility, research gaps, policies and human-AI partnerships. The conclusion emphasizes responsible, human-centric integration of AI to enhance pedagogy and learner experience.
TopAi-Driven Innovations In Language Assessment
Natural language processing (NLP) has revolutionized the field of automated essay scoring (AES). Utilizing machine learning algorithms and linguistic analysis, NLP enables the objective evaluation of essays based on various criteria such as grammar, coherence, and argumentation. This innovation not only enhances efficiency but also provides consistent and unbiased scoring, making it a powerful tool for language assessment (Hussein, Hassan, & Nassef, 2019; Sethi & Singh, 2022; Shermis & Burstein, 2013; Valenti, Neri, & Cucchiarelli, 2003).
Key Terms in this Chapter
Adaptive Testing: A type of testing that adjusts the difficulty of questions based on the learner's performance, enabled through AI algorithms for personalized assessment.
Artificial Intelligence (AI) in Language Learning Assessment: The integration of artificial intelligence (AI) technologies with traditional teaching methods to enable personalized and automated assessment and feedback mechanisms in language learning.
Speech Recognition: The application of AI to transcribe and interpret human speech, used in language learning for pronunciation practice and assessment.
Multimodal Analytics: The analysis of multiple types of data, such as text, speech, and visual information, to provide comprehensive insights and assessment in language learning.
Learning Analytics Dashboards: Tools that collect and analyze data on students' learning outcomes and preferences, offering personalized learning paths and providing educators with actionable insights.
Affective Computing: The study and development of systems that can recognize and interpret human emotions, applied in language learning to understand and respond to learners' emotional states.
Automated Essay Scoring: A process that uses AI-driven algorithms to grade and evaluate written essays, allowing for consistent and unbiased scoring.
Automated Writing Evaluation (AWE): AWE programs use AI algorithms to assess written text, providing immediate feedback on grammar, syntax, and structure. They support learners in refining their writing skills while offering educators a scalable and efficient solution for evaluating writing proficiency.