In recent years, the integration of artificial intelligence (AI) into educational environments, particularly within Learning Management Systems (LMS), has significantly increased. Despite the rapid growth of this forward-thinking technology, many AI tools are still used as isolated tools with limited synergy with the expected competency models of digital learning environments. This often leads to cognitive overload for learners, adversely affecting their motivation and learning outcomes. The challenge lies in integrating AI in teaching and learning processes in such a way, that they align with the required competencies and learning objectives of the environment and operate in an output-oriented manner. An integration of AI that is valid in terms of learning objectives can ensure that the technology does not only operate as a side tool, but rather as an effective extension of the learning environment, enhancing both teaching and learning processes. To achieve this goal, it is essential to integrate instructional designs into teaching and learning that are specifically tailored to be learner-focused, need based and valid in terms of learning objectives. Incorporating such instructional considerations in the development of AI tools in adaptive learning environments is crucial for creating an efficient environment that supports both educators and learners. In this paper, we present how an AI-based recommendation system, which is valid to the curriculum and learning objectives, can generate a positively significant increase in competencies among learners.

Authors: Sam Toorchi Roodsari, Shahram Azizi Ghanbari

• Date of Conference: 4-6 November 2024
• DOI: 10.20533/LICE.2024.0023
• ISBN: 978-1-913572-74-7
• Conference Location: St Anne’s College, University of Oxford, Oxford, UK