In the latest period as the pandemic has spread worldwide, the need to provide students and learners with effective and personalized didactical resources in order to maintain and even empower the potential level of training has significantly increased: specifically, the major necessity is related to the high potentialities in adopting educational models and methods based on artificial intelligence to reduce the cognitive effort made by teachers when searching, interacting with peers, creating and organizing educational resources. Therefore, nowadays there is a greater diffusion of Artificial Intelligence (AI) techniques and methods for educational purposed and platforms. Every training institution, school and organization definitively requires both advanced, smart and user-friendly technological tools to foster education and training process in the present historical situation. AI algorithms have proved to be crucial in different and heterogeneous research domains: among these methods, recommendation systems have been studied and applied in the e-learning domain. Nevertheless, AI and deep learning (DL) models suffer from trust issues, which poses specific problems which are different than in other kinds of technologies. Thus, if similar concepts are not addressed, there is the concrete to obtain models that perform well but exhibit inconsistent behaviors causing confusion during the interaction process between user and system: such confusion can even be a reason for abandonment of AI technology. Among these intelligent algorithms, content filtering mechanisms such as recommendation systems allow to provide explicit and interactive mechanisms to foster effective cooperation between the users and the system. In this paper, we describe the current human interaction level within the recommendation system (RS) for filtering educational resources of the educational platform ”WhoTeach”, which aims to create and personalize educational courses. In particular, we report the results of a experimentation carried out on a sampled set of users in the University of Milan - Bicocca. Then, we report a set of techniques and measures, identified in the state of the art, to improve the human computer interaction level with intelligent recommender systems (RS). In addition, we provide design guidelines for RS to introduce an acceptable interaction level, according to a well-known methodology in Human - AI Interaction. Based on that, we report and motivate the techniques we have chosen, in order to significantly empower the teacher - AI interaction and collaboration within the educational platform WhoTeach.

Matamoros Aragon, R., Marconi, L., Zoppis, I., Manzoni, S., Mauri, G., Musiu, E. (2021). Enhancing Teachers-AI Collaboration: Human Computer Interaction Techniques for Recommender Systems in Educational Platforms. Intervento presentato a: Convegno Nazionale Didamatica 2021 - “Artificial Intelligence for Education”, Palermo.

Enhancing Teachers-AI Collaboration: Human Computer Interaction Techniques for Recommender Systems in Educational Platforms

Matamoros Aragon R.;Marconi L.;Zoppis I.;Manzoni S.;Mauri G.;
2021

Abstract

In the latest period as the pandemic has spread worldwide, the need to provide students and learners with effective and personalized didactical resources in order to maintain and even empower the potential level of training has significantly increased: specifically, the major necessity is related to the high potentialities in adopting educational models and methods based on artificial intelligence to reduce the cognitive effort made by teachers when searching, interacting with peers, creating and organizing educational resources. Therefore, nowadays there is a greater diffusion of Artificial Intelligence (AI) techniques and methods for educational purposed and platforms. Every training institution, school and organization definitively requires both advanced, smart and user-friendly technological tools to foster education and training process in the present historical situation. AI algorithms have proved to be crucial in different and heterogeneous research domains: among these methods, recommendation systems have been studied and applied in the e-learning domain. Nevertheless, AI and deep learning (DL) models suffer from trust issues, which poses specific problems which are different than in other kinds of technologies. Thus, if similar concepts are not addressed, there is the concrete to obtain models that perform well but exhibit inconsistent behaviors causing confusion during the interaction process between user and system: such confusion can even be a reason for abandonment of AI technology. Among these intelligent algorithms, content filtering mechanisms such as recommendation systems allow to provide explicit and interactive mechanisms to foster effective cooperation between the users and the system. In this paper, we describe the current human interaction level within the recommendation system (RS) for filtering educational resources of the educational platform ”WhoTeach”, which aims to create and personalize educational courses. In particular, we report the results of a experimentation carried out on a sampled set of users in the University of Milan - Bicocca. Then, we report a set of techniques and measures, identified in the state of the art, to improve the human computer interaction level with intelligent recommender systems (RS). In addition, we provide design guidelines for RS to introduce an acceptable interaction level, according to a well-known methodology in Human - AI Interaction. Based on that, we report and motivate the techniques we have chosen, in order to significantly empower the teacher - AI interaction and collaboration within the educational platform WhoTeach.
No
paper
Scientifica
Human - Computer Interaction, Human - AI Interaction, Recommender Systems, Educational Platforms, WhoTeach
English
Convegno Nazionale Didamatica 2021 - “Artificial Intelligence for Education”
Matamoros Aragon, R., Marconi, L., Zoppis, I., Manzoni, S., Mauri, G., Musiu, E. (2021). Enhancing Teachers-AI Collaboration: Human Computer Interaction Techniques for Recommender Systems in Educational Platforms. Intervento presentato a: Convegno Nazionale Didamatica 2021 - “Artificial Intelligence for Education”, Palermo.
Matamoros Aragon, R; Marconi, L; Zoppis, I; Manzoni, S; Mauri, G; Musiu, E
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/390669
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