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Table 2 Factors of learner–instructor interaction, scenario titles, and scenario summaries

From: The impact of artificial intelligence on learner–instructor interaction in online learning

ID Factor of learner–instructor interaction Scenario title Scenario summary
1 Communication AI Teaching Assistant (Goel & Polepeddi, 2016) AI answers student questions before, during, or after online courses based on answers to questions gathered in previous courses
2 AI Companion (Woolf et al., 2010) AI emotionally supports students who are concerned about their grades and workload, and provides assistance when students use language related to self-destructive behavior
3 AI Grading Assistance (Perin & Lauterbach, 2018) AI helps TAs quickly grade assignments by offering suggestions that they should choose to accept or change for each question
4 AI Peer Review AI normalizes peer review grades by keeping students’ holistic profiles in mind, and by comparing each students’ history of peer reviews with others as well as with peer reviews from previous iterations of the course
5 Support AI Analytics (Luckin, 2017) AI provides an analysis of students’ clickstream, quiz, login/logout, and eye-tracking data to instructors
6 Intelligent Suggestions (Luckin, 2017) AI suggests study materials and strategies to students based on an analysis of students' clickstream and quiz performance data
7 AI Group Project Organizer AI helps write meeting minutes using speech recognition, suggests action plans from group discussions through text summarization, and gives editing tips based on assignment data from previous iterations of the course
8 Adaptive Quiz (Ross et al., 2018) AI provides students with a personalized set of exercise problems that suits their level of knowledge
9 Presence Virtual Avatar (Heidicker et al., 2017) AI communicates facial expressions and body language without explicitly using a student’s camera feed through a virtual avatar
10 AI Breakout Room Matching AI matches students in breakout rooms in a way that optimizes discussion by analyzing microphone data (e.g., frequency, length and tone)
11 AI Facial Analytics (Aslan et al., 2019) AI gauges students’ emotions without sharing videos, and notifies the instructor in real-time when a specific student seems especially distressed or unengaged