How Machine Learning and AI Can Benefit Higher Ed

With the endless stories about ChatGPT in the news and theories on it could negatively affect teaching and learning in higher ed, artificial intelligence (AI) and machine learning (ML) are becoming increasingly topical among college and university leaders. However, few headlines highlight how machine learning and AI can benefit Higher Ed.

To help higher ed decision-makers avoid getting too caught up in the negative hype, Dr. Drumm McNaughton discusses these technologies with Michael Feldstein, chief accountability officer at e-Literate. Michael shares easy-to-understand analogies to explain how and why AI functions the way it does, the problems AI can solve in higher ed, the importance of not having AI replace but augment human workers in district processes, and the benefits and shortcomings of tools such as ChatGPT.

• Combining functions like Google's "type-ahead" algorithm and plagiarism detectors can produce tools that will effectively evaluate whether students paraphrase well.
• Software that analyzes multiple patterns of student answers with well-written questions and learning objects can catch systemic errors easily missed by faculty and staff. For example, it can identify mistakes students make by learning whether they are progressing toward competency or where they might get stuck. It can also determine if there's a problem with a particular part of the course design where students are having difficulties.
• Combining AI technologies that can more quickly identify students who might be in danger of dropping out because they've been missing class with data that the average person might miss. For example, if these students work two jobs and commute to campus, AI can help discover new patterns.
• Chatbots are helping higher ed find students who need help in a way that might prevent them from getting what they need. For questions that require a human to answer, chatbots direct the students to humans who can help them. This ensures that student support professionals respond to students who need them instead of those who need a quick answer.
• Sophisticated statistical analysis can improve chatbot functionalities by measuring specific parameters, like how much it matters that students receive quick responses or the kinds of reactions that chatbots can more successfully help students achieve their goals or make more effective actions than others. This leads to automating this insight to refine itself.
• When recording interactions with students, higher ed needs to ensure it explicitly encodes the information for the machine and human to learn since there are human contexts that software doesn't understand.
• Higher ed leaders should avoid wasting the potential of their ultimate knowledge workers by having them conduct many menial tasks that software can perform. However, they also shouldn't feel shackled to their legacy technology and realize that newer solutions can suggest better approaches than their current use.

Michael Feldstein has been an educator and a student of educational technology for over 30 years. He currently serves as chief accountability officer at e-Literate, providing strategic consulting about technology-enabled education to leaders at universities, EdTech companies, and non-profit organizations. He also writes and manages its popular group weblog on educational technology.

Before e-Literate, he provided strategic planning and product management consulting for universities, among other groups, as a partner at MindWires Consulting. He has also held the positions of MindTap's senior program manager at Cengage Learning and principal product strategy manager for Academic Enterprise Solutions (formerly Academic Enterprise Initiative, or AEI) at Oracle Corporation.

Michael was also an assistant director at the SUNY Learning Network, where he oversaw blended learning faculty development and was part of the leadership team for the LMS platform migration efforts. Before SUNY, he was co-founder and CEO of a company that provided e-learning and knowledge management products and services to Fortune 500 corporations, with an emphasis on software simulations.

Michael has been a member of the Sakai Foundation's Board of Directors, a participant in the IMS, and a member of eLearn Magazine's Editorial Advisory Board. He is a frequently invited speaker on various e-learning-related topics, including e-learning usability, the future of the LMS, ePortfolios, and edu-patents for organizations ranging from the eLearning Guild to the Postsecondary Electronic Standards Council. In addition, he has been interviewed as an e-learning expert by various media outlets, including The Chronicle of Higher Education, the Associated Press, and U.S. News & World Report.

Link to Transcript

Dr. Drumm McNaughton, host and consultant to higher ed institutions. To learn more about his services and other thought leadership pieces, visit his firm's website, https://changinghighered.com/.

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Keywords: #AIinHigherEd #ChatGPT #MachineLearning