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AI-Based Multilingual Interactive Exam Preparation

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 349)

Abstract

Our previous analysis on 26 languages which represent over 2.9 billion speakers and 8 language families demonstrated that cross-lingual automatic short answer grading allows students to write answers in exams in their native language and graders to rely on the scores of the system [1]. With lower deviations than 14% (0.72 points out of 5 points) on the corpus of the short answer grading data set of the University of North Texas [2], our natural language processing models show better performances compared to the human grader variability (0.75 points, 15%). In this paper we describe our latest analysis of the integration and application of a multilingual model in interactive training programs to optimally prepare students for exams. We present a multilingual interactive conversational artificial intelligence tutoring system for exam preparation. Our approach leverages and combines learning analytics, crowdsourcing and gamification to automatically allow us to evaluate and adapt the system as well as to motivate students and increase their learning experience. In order to have an optimal learning effect and enhance the user experience, we also tackle the challenge of explainability with the help of keyword extraction and highlighting techniques. Our system is based on Telegram since it can be easily integrated into massive open online courses and other online study systems and has already more than 400 million users worldwide [3].

Keywords

  • Artificial intelligence in education
  • Cross-lingual short answer grading
  • Conversational AI
  • Keyword extraction
  • Natural language processing

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Schlippe, T., Sawatzki, J. (2022). AI-Based Multilingual Interactive Exam Preparation. In: Guralnick, D., Auer, M.E., Poce, A. (eds) Innovations in Learning and Technology for the Workplace and Higher Education. TLIC 2021. Lecture Notes in Networks and Systems, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-90677-1_38

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