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Artificial intelligence at universities in Poland

Stachowicz-Stanusch A., Amann W.

Organizacja i Zarządzanie : kwartalnik naukowy

2018

nr 2

65--82

Artificial intelligence (AI) technologies are one of top investment priorities in these days. They are aimed at finding applications in fields of special value for humans, including education. Chatbots are one of those AI-driven solutions that support learning and teaching processes also in higher education institutions. In this paper there are presented two cases of chatbot technology implementation at Polish universities. Chatbots develop students’ technical and programming skills, but also provide the possibility of gaining linguistic expertise. However, a chatbot’s teaching mastery depends also on its users. That is why it is important to get students to truly understand AI systems and feel responsible for the conversation. But above all, we should ensure that chatbots respect human and civil rights.

Assessment of the didactic measurement results using FCM type networks

Gumińska M., Madejski J.

Archives of Materials Science and Engineering

2009

Vol. 39, nr 1

45-52

Purpose: The paper presents students egzamination system developer for the scaleable e-learning system. Organisation of the teaching and examination processes, as well as the implementation details are described. An intelligent system based on one of the Artificial Intelligence methods - FCM (Fuzzy Cognitive Maps) type network is being developed within the framework of current work on the e-learning process topic, to model the behaviour and functioning the system as a whole. Design/methodology/approach: The intelligent examination system for students was developed based on mechanism derived from HotPotatoes system. Programming languages like PHP and JavaScript were also used. Fuzzy Cognitive Maps were used to model the e-learning process and an example of the system use is presented. Findings: The project effect is the intelligent examination system supporting the statistical analysis of the difficulty level of test problems, generating comments and materials individually for every user. The didactic process was modelled using FCM method. Practical implications: Reduction of test checking time consumption, individual attitude to every student, score advised to the students along with the comments pertaining to the wrong answers and recommended study topics - all immediately after the test, sent to the student's mailbox. Originality/value: Employment of FCM AI tool for evaluation of the teaching process effectiveness.