Four most common AI technology types in cyberlearning: intelligent tutoring systems, machine learning, speech/vision/natural interaction, and social robotics and avatars¶

Theory · All theories
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The report's AI theme defines the portfolio's most common technologies: 'Intelligent Tutoring Systems, in which a computational agent supports and guides students as they work to learn challenging subject matter'; machine learning applying statistical techniques to learner data; speech, vision, and natural interaction analyzing learner speech, visual scenes, and eye-gaze; and social robotics and avatars providing 'an artificial learning partner to a student or group of students'.

Design Implications¶

Context¶

Requirements¶

• Advancing these technologies requires conjunction with learning scientists and experts in equity and subject matter or educational contexts

Constraints¶

• Cyberlearning AI projects push beyond the boundaries of a conventional ITS, combining AI with learning theories in hybrid human-AI systems

Target Learners¶

• Students learning challenging subject matter, including underrepresented groups in STEM

Target Learning Objectives¶

• Learning challenging subject matter; assessment of what students know and can do; workforce learning

Claims¶

• Respondents anticipated using LLMs most often (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and ASR (10%) [+W]

Related Theories¶

• Three-layer definition of AI for learning: computational intelligence, specific capabilities, and a futures toolkit
• Five new design concepts for AI in learning: orchestrating, augmenting, expanding natural interactions, broadening competencies, and revealing connections
• The electric bike vision of human-centered educational technology
• Three learning-theory areas organize cyberlearning theory work: collaborative learning, embodied learning, and identity

Examples¶

• Amira AI reading tutor
• A co-orchestration technology ecosystem combining intelligent tutoring systems, peer tutoring, and a teacher-facing orchestration tool.

Key Sources¶

• Center for Innovative Research in Cyberlearning (2020). Ambitious mashups: Reflections on a decade of cyberlearning research. [Report]. Digital Promise. https://circlcenter.org/resources/reflections-report/