Think Tank I - Trends in Teaching and Education

For the this post, I selected an article that discusses the trends in learning (Pelletier et al., 2023). This report covers social, technological, economic, environmental, and political trends in teaching. More flexible learning options are the top trend cited. In technology, the impact of AI in teaching and learning is getting the most attention. The United States has reached a critical stage in the high cost of education. Unsurprisingly, affordability is the top economic trend in the report. The hype around climate change continues to stir panic. A trend identified is the impact on daily life. In the political arena, government weaponization of disinformation is the leading trend, not surprisingly. 

I will focus on the technology trend to align with my interest in technology. The top list of technologies having an impact is artificial intelligence (AI) technology-enabled learning modalities (including hybrid on-site and online attendance called HyFlex). Two other areas noted in the trends result from trends in technology to become enablers, specifically for micro-credentialing and providing an improved student experience. 

The largest and most exciting of the trends refer to AI in education. Pelletier et al. (2023) identified AI technologies' increased sophistication and democratization, particularly predictive analytics and generative AI. These are the two areas that caught my attention in the report.

Predictive AI systems are fueling individual learning and improving the experiences of students. Examples of this technology are tools that save students time by coaching students. These are essentially feedback tools, such as software that helps students write better by assessing the quality of an assignment and subsequently providing students with recommendations for improvement, as well as descriptions of why the students should consider the feedback. On the education administration side of the learning equation, AI systems help assess students' curriculums, predict success, and provide services to students. Examples of these services are AI-fueled tutoring and chatbots that are available to students whenever needed instead of solely relying on live administrative personnel that are generally only available during limited working hours.

Generative AI is receiving the most hype from the public and the most concern in pedagogical contexts. Two educational examples that use generative AI applications are a new module in a Business Management Master’s program, wherein digital marketing course materials center around new technologies for customer engagement, the metaverse, and AI data analysis. As a great example of the digital twin concept, a kinesiology course uses virtual reality (VR) to allow the student to interact with an AI agent acting as an actual patient. The system leverages interfaces and virtual apparatus to interact with the virtual patient, including creating custom interactions using natural language processing (NLP) techniques.

 One challenge to the technology trends in education is adoption. Integrating any technology into any business (education is, unfortunately, a business) is challenging. Choosing the right technology is perhaps the first hurdle. A historical example would be which investment in videotaping and playback equipment was better, VHS or Betamax. Betamax was technically better in that example, but VHS had manufacturing and marketing advantages. VHS won the battle, and all those institutions that may have bet on the better technology eventually had to reinvest in VHS equipment. Another challenge is integrating the right technologies in the right way into the curriculum and teaching. It is an ongoing challenge for universities to adopt and integrate new technology cost-effectively that enriches the learning experience for students.

Ethical issues are a third, but critically important, blocker for generative AI. As Pelletier et al. (2023) note in the report, universities are paralyzed by the potential integrity impact of generative AI. An art student can use generative AI to create artwork for an assignment instead of creating original art. A music major can use generative AI to write a complete score. There are generative AI systems that can generate complete or near-complete dissertations, completely denigrating the integrity of the doctoral process. In some sense, these are existential issues for education. Unfortunately, these are not the only ethical issues in AI. Coupled with potential bias, potential malfeasance (such as AI-assisted hacking), and a tendency for hallucinations, educators are facing a seemingly insurmountably gauntlet that jeopardizes the productive and legitimate induction of AI into teaching and learning environments. 

References

Pelletier, K., Robert, J.,  Muscanell, N., McCormack, M., Reeves, J., Arbino, N., Grajek, S., Birdwell, T., Liu, D., Mandernach, J., Moore, A., Porcaro, A., Rutledge, R., &  Zimmern, J. (2023, May 8). 2023 EDUCAUSE horizon report: Teaching and learning edition. EDUCAUSE. https://www.educause.edu/horizon-report-teaching-and-learning-2023