The Power of Collaboration: Enhancing Learning in Higher Education with Generative AI

Nothing new here, friends. We know that collaborative learning has been a topic of interest in the field of education writ large for decades, much longer if we go all the way back to Vygotsky. We know that students’ working together to achieve shared learning goals has a lot of benefits, including improved academic performance, enhanced critical thinking skills, and increased social interaction. But I would like to add a wrinkle to this tapestry of knowledge. As we turn future-forward I submit to you that the infusion of generative AI into collaborative learning practices has the potential to revolutionize the way we approach teaching and learning in higher education – and that we can assess the same rhetorical behaviors and learning outcomes through collaborative assessment measures.

Snapshot: A History of Collaborative Learning

The roots of collaborative learning can be traced back to the work of educational theorists such as John Dewey and Lev Vygotsky in the early 20th century. Dewey emphasized the importance of social interaction and experiential learning in the educational process, while Vygotsky’s concept of the “zone of proximal development” highlighted the role of more knowledgeable peers in facilitating learning^2. Over time, collaborative learning has evolved to encompass a wide range of strategies, including project-focused group learning, peer reviewing, and team-based learning.

Collaborative Learning in Practice

In higher education, collaborative learning can be implemented in various ways. One effective approach is to incorporate – wait for it — group projects into course assignments. By working together on a shared task, students can learn from each other’s perspectives, develop teamwork skills, and take ownership of their learning³. Several recent studies and reports have noted the success of project-focused group work, when we design the assignments and assessments appropriately to engage students individually and as a community of learners. This video is one is my faves; I’m a Ethan Mollick devotee. If you haven’t yet, check out his book, Co-Intelligence: Living and Working with AI. It’s written from a non-technical perspective, making it an enjoyable and easy read. In his book, he discusses the value of collaboration with AI as “a partner, not a replacement” (174) and writes about flipping a classroom with AI, an idea I have toyed with for a couple of years too. Stay tuned for more on that soon!

A strategy I have embraced is peer reviewing activities, all along a course design and pace in low stakes and high stakes assignments. There are two opportunities I see here: (1) students helping each other understand course material, and (2) produce work that is based on learning outcomes and course expectations. What I mean by this is that students work with each other to meet the learning outcomes for specific assignments. In a writing-centered classroom (across disciplines), this often looks like students helping each other successfully embrace not only content but the rhetorical aspects of communicating their synthesis of that content. Peer reviewing not only benefits the struggling students but also the students who grasp the material well, given what we know about the measure of knowing a topic is how well one can teach it! Later in this post, I will provide some suggestions and examples of guided peer reviews that you can edit and use in your own learning space. A good resource is also footnoted here⁴.

Let’s Add Generative AI

The increasing ubiquity of generative AI in all of our professional and personal spaces presents exciting (and sometimes frightful) opportunities for enhancing collaborative learning in higher education. Chatbots powered by large language models, such as ChatGPT and Claude, can serve as peer collaborators, providing personalized feedback and guidance to students as they work through course material⁵. These AI assistants can also help facilitate discussions by offering prompts and suggestions, encouraging students to engage more deeply with the content².Moreover, generative AI can be used to create interactive learning experiences, such as simulations and role-playing scenarios. By immersing students in realistic situations, these tools can help them develop critical thinking and problem-solving skills in a collaborative setting³. Additionally, AI-generated content, such as summaries and explanations, can be used as starting points for group discussions, allowing students to critically analyze and build upon the information provided⁴.

Let’s Add Prompt Engineering for Collaborative Learning

Prompt engineering, the rhetorical art of crafting effective prompts for generative AI models, can be a powerful tool for enhancing collaborative learning. By providing well-designed prompts, instructors can guide students towards specific learning objectives and encourage them to engage in deeper discussions¹. We have to be aware, though, of how to get ethical outputs. I wrote about four qualifiers for cultivating ethical outputs last week.

For example, an instructor could prompt students to analyze a case study from multiple perspectives and then collaborate to develop a comprehensive solution. Another application of prompt engineering in collaborative learning is the creation of personalized learning experiences. By tailoring prompts to individual students’ needs and interests, generative AI can help foster a sense of ownership and engagement in the learning process⁵.

Ten More Examples of Collaborative Learning (It’s OK if it’s TL;DR)

Facilitating real-time language translation and transcription of discussions, enabling students from diverse backgrounds to participate more effectively in group work and discussions
Analyzing the strengths and weaknesses of individual students and adjusting the difficulty level of content or activities in real-time to optimize the learning process and provide a more personalized experience
Providing intelligent tutoring systems that can offer personalized feedback and guidance to students as they work through course material, freeing up instructors to focus on higher-level facilitation
Generating interactive learning experiences, such as simulations and role-playing scenarios, that immerse students in realistic situations and help them develop critical thinking and problem-solving skills in a collaborative setting
Creating AI-generated content, such as summaries and explanations, that can serve as starting points for group discussions, allowing students to critically analyze and build upon the information provided – great idea to build a GPT. Here’s one for first-year writing
Offering prompts and suggestions to facilitate discussions and encourage students to engage more deeply with the content
Providing personalized learning experiences by tailoring prompts to individual students’ needs and interests, fostering a sense of ownership and engagement in the learning process
Generating personalized learning materials and exercises: Prompt engineering can be used to create customized learning content tailored to the specific needs and learning styles of individual students or groups. Generative AI models can analyze student data and generate personalized prompts to produce targeted exercises, explanations, or examples that facilitate more effective collaborative learning experiences
Facilitating peer-to-peer learning and feedback: Gen-AI models can be prompted to analyze student work and provide feedback and suggestions for improvement. This can enhance peer-to-peer learning by enabling students to receive AI-generated feedback on their contributions, fostering a more collaborative learning environment
Creating interactive role-playing scenarios: Prompt engineering can be used to generate immersive role-playing scenarios or simulations that allow students to collaboratively practice real-world problem-solving and decision-making skills. Generative AI models can dynamically adapt these scenarios based on student inputs, providing a more engaging and interactive collaborative learning experience

Challenges and Considerations

To be clear, friends, I know the integration of generative AI into collaborative learning holds great promise, but that it also presents several challenges that must be addressed. As educators, we have to consider data privacy, bias, and unethical outputs as we explore and encourage our students to explore. Additionally, faculty members must be trained in the effective use of these technologies to ensure that they are integrated seamlessly into the learning process And, might I add, we need to be compensated for this training.

Final Thoughts for Now

I hear you, and I get it, friends. I’ve not stumbled upon something new LOL. Collaborative learning has long been recognized as a powerful tool for enhancing student engagement and achievement in higher education. With the exponential infusions of generative AI into all communication spaces and the practice of prompt engineering, the possibilities for collaborative learning are expanding exponentially as well. By incorporating these technologies into their teaching practices, faculty colleagues can create dynamic, engaging learning environments that prepare students for success in the 21st century. Here are some references (not perfectly formatted) and resources.

Thanks for reading; let me know how your own tinkering goes — Jeanne

1 Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365-379.
2 Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
3 Barkley, E. F., Cross, K. P., & Major, C. H. (2014). Collaborative learning techniques: A handbook for college faculty. John Wiley & Sons.
4 Topping, K. J. (2005). Trends in peer learning. Educational Psychology, 25(6), 631-645.
5 Dwivedi, Y. K., Hughes, L., Ismagilova, E., Khan, G. F., Radovic Markovic, M., Brüntje, D., … & Viglia, G. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.