The iTalk2Learn tutoring system was quite an interdisciplinary project that combined ML, instructional design, NLP, educational psychology, and many others. Chapter 5 in #AI, The Rise of Lightspeed Learners, covers several essential aspects of using AI in human interactions.
It appears that the latest #LLM-based projects in conversational design, instruction, or other forms of interaction might need a similar engagement scope. While the initial applications of LLMs, GPT, and similar technologies shine in many areas of #NLP, conversational applications lack dialogue design, tone, and engagement. The interactions with #metaverse #avatars or bots are often clumsy and do not feel engaging.
There is a large amount of research that focuses on AI-assisted instruction and tutoring. Key issues include proper content sequencing, task management, hint design, and feedback analytics. Instructional design is often based on several principles, including domain-specific scenarios, conversation (#chat) management, content characteristics, skills, and difficulty-level modeling for individual domains.
Another area of research in AI-driven instructional systems is concerned with modeling students’ emotions. The models can be created explicitly from surveys or implicitly from user input sentiment analysis.
#ai #chatgpt #instructionaldesign #tutoring #tutoringsystems #aiassistant #ML #deeplearning #instructionaltechnology
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