Interacting with educational chatbots: A systematic review

By Mohammad Amin Kuhail, Nazik Alturki, Salwa Alramlawi, Kholood Alhejori. Education and Information Technologies : Official Journal of the IFIP technical committee on Education, 2022

689 citations recorded by the source corpus.

Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/978e4de83cf1fa692d3fc505e6ece67877812e78

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Citations in the corpus

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions (2023, background): “When developing generative AI technologies, it is also essential to adhere to user-focused design principles while taking into account the social, emotional, cognitive, and pedagogical dimensions [65].”
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey (2023, background): “Moreover, some researchers high-light the lack of sufficient datasets as a common challenge in educational chatbots leading to learning difficulties and frustration [16].”
Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA (2023, neutral): “Chatbots and language models like ChatGPT are some AI-powered tools that can enhance the educational experience (Kuhail et al., 2023).”
Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT (2023, background): “…to predefined dialogue paths or feature simplistic question-and-answer dialogue structures (for a review on existing educational chatbots, see Kuhail et al., 2022), this exponentially advanced chatbot can generate answers according to the context of a given prompt, and thus, can engage in a…”