Home Community MIT faculty, instructors, students experiment with generative AI in teaching and learning

MIT faculty, instructors, students experiment with generative AI in teaching and learning

MIT faculty, instructors, students experiment with generative AI in teaching and learning

How can MIT’s community leverage generative AI to support learning and work on campus and beyond?

At MIT’s Festival of Learning 2024, faculty and instructors, students, staff, and alumni exchanged perspectives concerning the digital tools and innovations they’re experimenting with within the classroom. Panelists agreed that generative AI must be used to scaffold — not replace — learning experiences.

This annual event, co-sponsored by MIT Open Learning and the Office of the Vice Chancellor, celebrates teaching and learning innovations. When introducing recent teaching and learning technologies, panelists stressed the importance of iteration and teaching students learn how to develop critical pondering skills while leveraging technologies like generative AI.

“The Festival of Learning brings the MIT community together to explore and rejoice what we do every single day within the classroom,” said Christopher Capozzola, senior associate dean for open learning. “This 12 months’s deep dive into generative AI was reflective and practical — one more remarkable instance of ‘mind and hand’ here on the Institute.”  

Incorporating generative AI into learning experiences 

MIT faculty and instructors aren’t just willing to experiment with generative AI — some imagine it’s a vital tool to organize students to be competitive within the workforce. “In a future state, we’ll know learn how to teach skills with generative AI, but we have to be making iterative steps to get there as a substitute of waiting around,” said Melissa Webster, lecturer in managerial communication at MIT Sloan School of Management. 

Some educators are revisiting their courses’ learning goals and redesigning assignments so students can achieve the specified outcomes in a world with AI. Webster, for instance, previously paired written and oral assignments so students would develop ways of pondering. But, she saw a chance for teaching experimentation with generative AI. If students are using tools resembling ChatGPT to assist produce writing, Webster asked, “how will we still get the pondering part in there?”

One among the brand new assignments Webster developed asked students to generate cover letters through ChatGPT and critique the outcomes from the attitude of future hiring managers. Beyond learning learn how to refine generative AI prompts to provide higher outputs, Webster shared that “students are pondering more about their pondering.” Reviewing their ChatGPT-generated cover letter helped students determine what to say and learn how to say it, supporting their development of higher-level strategic skills like persuasion and understanding audiences.

Takako Aikawa, senior lecturer on the MIT Global Studies and Languages Section, redesigned a vocabulary exercise to make sure students developed a deeper understanding of the Japanese language, slightly than good or unsuitable answers. Students compared short sentences written by themselves and by ChatGPT and developed broader vocabulary and grammar patterns beyond the textbook. “Such a activity enhances not only their linguistic skills but stimulates their metacognitive or analytical pondering,” said Aikawa. “They need to think in Japanese for these exercises.”

While these panelists and other Institute faculty and instructors are redesigning their assignments, many MIT undergraduate and graduate students across different academic departments are leveraging generative AI for efficiency: creating presentations, summarizing notes, and quickly retrieving specific ideas from long documents. But this technology also can creatively personalize learning experiences. Its ability to speak information in other ways allows students with different backgrounds and skills to adapt course material in a way that’s specific to their particular context. 

Generative AI, for instance, might help with student-centered learning on the K-12 level. Joe Diaz, program manager and STEAM educator for MIT pK-12 at Open Learning, encouraged educators to foster learning experiences where the coed can take ownership. “Take something that youngsters care about they usually’re keen about, they usually can discern where [generative AI] won’t be correct or trustworthy,” said Diaz.

Panelists encouraged educators to take into consideration generative AI in ways in which move beyond a course policy statement. When incorporating generative AI into assignments, the secret is to be clear about learning goals and open to sharing examples of how generative AI could possibly be utilized in ways in which align with those goals. 

The importance of critical pondering

Although generative AI can have positive impacts on educational experiences, users need to grasp why large language models might produce incorrect or biased results. Faculty, instructors, and student panelists emphasized that it’s critical to contextualize how generative AI works. “[Instructors] try to clarify what goes on within the back end and that basically does help my understanding when reading the answers that I’m getting from ChatGPT or Copilot,” said Joyce Yuan, a senior in computer science. 

Jesse Thaler, professor of physics and director of the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions, warned about trusting a probabilistic tool to provide definitive answers without uncertainty bands. “The interface and the output must be of a form that there are these pieces which you can confirm or things which you can cross-check,” Thaler said.

When introducing tools like calculators or generative AI, the college and instructors on the panel said it’s essential for college students to develop critical pondering skills in those particular academic and skilled contexts. Computer science courses, for instance, could permit students to make use of ChatGPT for help with their homework if the issue sets are broad enough that generative AI tools wouldn’t capture the complete answer. Nonetheless, introductory students who haven’t developed the understanding of programming concepts must find a way to discern whether the data ChatGPT generated was accurate or not.

Ana Bell, senior lecturer of the Department of Electrical Engineering and Computer Science and digital learning scientist, dedicated one class toward the tip of the semester of Course 6.100L (Introduction to Computer Science and Programming Using Python) to show students learn how to use ChatGPT for programming questions. She wanted students to grasp why organising generative AI tools with the context for programming problems, inputting as many details as possible, will help achieve one of the best possible results. “Even after it gives you a response back, you’ve to be critical about that response,” said Bell. By waiting to introduce ChatGPT until this stage, students were able to take a look at generative AI’s answers critically because that they had spent the semester developing the talents to find a way to discover whether problem sets were incorrect or won’t work for each case. 

A scaffold for learning experiences

The underside line from the panelists throughout the Festival of Learning was that generative AI should provide scaffolding for engaging learning experiences where students can still achieve desired learning goals. The MIT undergraduate and graduate student panelists found it invaluable when educators set expectations for the course about when and the way it’s appropriate to make use of AI tools. Informing students of the training goals allows them to grasp whether generative AI will help or hinder their learning. Student panelists asked for trust that they might use generative AI as a start line, or treat it like a brainstorming session with a friend for a bunch project. Faculty and instructor panelists said they may proceed iterating their lesson plans to best support student learning and demanding pondering. 

Panelists from either side of the classroom discussed the importance of generative AI users being answerable for the content they produce and avoiding automation bias — trusting the technology’s response implicitly without pondering critically about why it produced that answer and whether it’s accurate. But since generative AI is built by people making design decisions, Thaler told students, “You could have power to alter the behavior of those tools.”


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