In this session, we will explore early prototypes of AI applications and discuss challenges related to ethics and privacy in higher education IT. These prototypes leverage machine learning in a way that empowers content discovery, course design and assessment, reflective teaching practices, and predictive analytics.
Outcomes: * Learn how AI will shape the future of teaching and learning * Discover new example of applied AI technologies * Learn about an effective data science pipeline for higher education IT * Reflect on the ethical and privacy challenges related to AI applications
Decibel Analysis for Research in Teaching (DART) is a software tool that analyzes classroom sound to predict with ~90% accuracy the quantity of time spent on Single Voice (e.g. lecture), Multiple Voice (e.g. pair discussion), and No Voice (e.g. clicker, question thinking) activities.
Helping instructors engage in reflective teaching practices with the support of machine learning
- Provide insight into course content
- Audio analysis pipeline, speech2text, sentence embedding,
- Use these data for instructional design, time spent in class
- Patterns of interactivity
Leveraging AI for Academic Advising
Using machine learning enables us to provide insights into how students might perform in specific classes based on similar students in the past.
Pilot Study – Examine advisor use of LIFT during their academic review process. Academic review occurs in the period of time between semesters when advisors are reviewing student’s academic plan and progress and determining potential interventions.
- Penn State uses Starfish
- Privacy and Ethics – Include technical review, bias elements are evaluated, investigating what decisions are being supported by the model, are differences experienced with predicated outcomes, are they accurate, what about consequences, privacy impact assessment helps to determine what is collected, how it’s uses