An analysis of teaching strategies and feedback for personalization in an Intelligent Tutoring System with Artificial Intelligence
Interactivity; Intelligent Tutoring Systems; Processos de ensino-aprendizagem; Learning; Teaching.
The use of Artificial Intelligence (AI) technology can help teachers personalize teaching and, consequently, help students in the learning process. AI-based technologies are supporting education and research has been carried out with a view to improving them and obtaining greater assertiveness from the developed predictive model. One of the AI technologies applied to education is the Intelligent Tutoring Systems (ITS). In this sense, this research aims to: Analyze the teaching strategies used and the feedback given for teaching customization in an ITS that uses AI. To meet the general objective, the following specific objectives were worked on: (1) identify the characteristics of interactivity and how it impacts the learning process; (2) identify and analyze the state of the art of AI application in STIs regarding the techniques used, objectives, difficulties encountered and assessment of effectiveness in learning; (3) evaluate the coherence and consistency of the teaching strategies used and the feedback generated by AI to personalize teaching; and, (4) identify the interactivity characteristics present in an ITS that uses AI. To build this dissertation, a series of articles were prepared and submitted for publication in journals evaluated in the upper strata of Qualis to reach each of the specific objectives. In this sense, the multipaper writing format was used to replace the traditional monographic style