AI-Driven Appbox to Facilitate Self-Assessment in an Intelligent Educational Environment

Intelligent learning environments integrate various functions for processing and analyzing learning data by leveraging learning analytics theory. Based on this data, numerous machine learning (ML) and artificial intelligence (AI) models are created to predict learning behavior and learning performance with main purpose to improve learning, assessment and teaching. The important role of self-assessment conducted by students in support of their learning progress and final results continues to be explored and discussed. Despite the availability of various educational software products, there is a need to develop intelligent applications to enhance students' self-assessment. The purpose of the paper is to present a conceptual framework and its subsequent implementation by developing a set with web applications based on ML and AI techniques. The applications are related to self-assessment of affective state, predicting and planning learning tasks, self-assessment of graphic objects, knowledge gaining from conversational assistant and self-assessment of learning performance.