Abstract
The recent advent of popular AI applications in educational contexts has sparked renewed interest in the question of AI and guided learning platforms as teaching tools. What are the possibilities for learning? In the attempt to answer that question, limitations of the field must be brought to full attention, as well an understanding of whether or not those limitations will continue into the immediate future; this research examines the technical evolution of artificial intelligence in education, from early symbolic reasoning systems to modern machine-learning-based chatbots. It then examines that evolution in terms of the key challenges it faced throughout, and points out any remaining unsolved challenges in student learning platforms–knowledge representation, student modeling, and human-like interaction. Though these platforms continue to evolve, in terms of these final barriers, only the human-like interaction aspect continues to make effective progress. Bridging the gaps between the Natural Language Processing tools popularized currently, structured knowledge representations, and student learning models will be crucial for developing AI tutors that are both effective and widely accepted.
Included in
Computer and Systems Architecture Commons, Education Commons, Engineering Education Commons