A Systematic Mapping of Affective Intelligent Tutor Systems in Engineering Education

This full research paper presents a systematic mapping of applications of affective intelligent tutor systems in engineering education. These systems employ techniques such as self-questionnaires, voice or facial recognition, system usage tracking, or combinations of these approaches. The data collection occurs through the sensors that measure input from devices, such as microphones, cameras, and body sensors. This paper presents a systematic mapping of affective intelligent tutoring systems in engineering courses and seeks to answer the following questions: Q1. Which engineering courses employ affective intelligent systems?; Q2. What secondary or auxiliary technologies, if any, do the authors use in combination with affective tutoring systems?; Q3. How do these studies perform emotion recognition?; Q4. What emotions do these studies recognize?; Q5. Which evaluation methods do the studies use? The research concentrated on primary sources, such as articles and conference proceedings published between 2010 and 2022 on established scientific databases, including ACM Digital Library, IEEE Xplore, Science Direct, and Springer. We identified 413 potential articles and narrowed the options down to 23 papers that fully matched the inclusion criteria. We contribute to the literature by answering the proposed questions and categorizing the existing papers to find directions for future research.