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 ought to 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 latest teaching and learning technologies, panelists stressed the importance of iteration and teaching students how you can develop critical considering skills while leveraging technologies like generative AI.
“The Festival of Learning brings the MIT community together to explore and have a good time what we do on daily basis within the classroom,” said Christopher Capozzola, senior associate dean for open learning. “This yr’s deep dive into generative AI was reflective and practical — yet 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 essential tool to organize students to be competitive within the workforce. “In a future state, we are going to know how you can teach skills with generative AI, but we should 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 considering. But, she saw a chance for teaching experimentation with generative AI. If students are using tools comparable to ChatGPT to assist produce writing, Webster asked, “how will we still get the considering part in there?”
One in all the brand new assignments Webster developed asked students to generate cover letters through ChatGPT and critique the outcomes from the angle of future hiring managers. Beyond learning how you can refine generative AI prompts to provide higher outputs, Webster shared that “students are considering more about their considering.” Reviewing their ChatGPT-generated cover letter helped students determine what to say and how you can 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, moderately than excellent or improper answers. Students compared short sentences written by themselves and by ChatGPT and developed broader vocabulary and grammar patterns beyond the textbook. “One of these activity enhances not only their linguistic skills but stimulates their metacognitive or analytical considering,” said Aikawa. “They must 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 can even creatively personalize learning experiences. Its ability to speak information in alternative 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, may also 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 scholar can take ownership. “Take something that children care about they usually’re captivated with, they usually can discern where [generative AI] may not 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’s 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 considering
Although generative AI can have positive impacts on educational experiences, users need to know 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 offer definitive answers without uncertainty bands. “The interface and the output must be of a form that there are these pieces that you may confirm or things that you may cross-check,” Thaler said.
When introducing tools like calculators or generative AI, the school and instructors on the panel said it’s essential for college students to develop critical considering 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 total answer. Nevertheless, introductory students who haven’t developed the understanding of programming concepts have to have the opportunity 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 MITx digital learning scientist, dedicated one class toward the top of the semester of Course 6.100L (Introduction to Computer Science and Programming Using Python) to show students how you can use ChatGPT for programming questions. She wanted students to know why establishing generative AI tools with the context for programming problems, inputting as many details as possible, will help achieve the very 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 have a look at generative AI’s answers critically because they’d spent the semester developing the talents to have the opportunity to discover whether problem sets were incorrect or may not work for each case.
A scaffold for learning experiences
The underside line from the panelists through 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 know whether generative AI will help or hinder their learning. Student panelists asked for trust that they’d 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 considering.
Panelists from each side of the classroom discussed the importance of generative AI users being liable for the content they produce and avoiding automation bias — trusting the technology’s response implicitly without considering 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 vary the behavior of those tools.”
