Identifying Success Criteria for Sustainable AI-based Online Laboratory Courseware System

The great transformation of e-learning during the Covid-19 pandemic has led to the emergence of new learning tools and virtual learning environments through Internet. Despite many advantages of e-Learning courseware systems such as availability, flexibility and accessibility, students of some sectors such as engineering, science and technology are facing several limitations in conducting their practical works remotely through online platform for laboratory experiments. The specific objective of this study was to come up with an updated success criteria and list of requirements that should be considered for developing a sustainable artificial intelligence-based online laboratory courseware system. Data for this study were collected using online questionnaire distributed to a group of e-Learning experts. NVivo software was used to analyze experts’ comments on how to construct the online laboratory courseware systems. This research revealed 16 basic design and development criteria for the online laboratory courseware system, which are distributed into eight sub-branches and organized into four primary aspects. These findings suggest that in general 30 accurate indicators for the design of an effective laboratory learning system (LLS) for engineering, science and technology sectors dealing with content management, assessment, accessibility and usability as well as the adoption of artificial intelligence techniques.