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How Educators Can Leverage Generative AI to Augment Teaching and Learning


By Sedinam Worlanyo, Senior Product Researcher and Eli Fogle, Senior Learning Design Consultant, Coursera 

Context 

The way we learn and therefore the way we must teach is changing rapidly. At the heart of this recent change lies Generative AI (GenAI), a technology that allows students to both learn and demonstrate their learning in entirely new ways. This disruption has created a transformative moment in education, and this new interplay between technology, learning, and assessment requires a rethinking of pedagogical practices and learner experiences.  

A key question we have heard from educators is: “How can we creatively empower students to harness the full potential of GenAI, while protecting against the risks it may pose?” 

Our team at Coursera conducted research to understand how our learners are using GenAI tools like ChatGPT in their learning. We sought perspectives about the extent to which use cases and behaviors helped or hindered learners. To adapt and evolve pedagogy to the changing learning landscape driven by GenAI, we conducted research to:

  • Understand specific behaviors for learners leveraging GenAI in assessments
  • Understand the spectrum of use cases and learner behaviors where GenAI could help or hinder learning 

Our research uncovered examples of learners leveraging GenAI to support their own learning in ways that mimic the pedagogical strategies of instructors, including differentiation, active learning, and metacognition. Read further for some key insights highlighted in learner case studies and discover how you, as educators, may be able to use GenAI to augment your teaching and learning.

N/B learner names have been changed to protect the identity of learners.
Sample prompts mirror learner prompts from research but are not written verbatim.

Learner Case Study 1

How Shira Created a Personalized Example to Clarify Concepts

Problem: Trouble understanding unfamiliar terminology and relating it back to examples provided to explain concepts.

Shira’s Story: As part of our learner research, we discovered Shira, who was struggling with understanding new terminology and connecting core concepts to the examples used in her course. In lectures and readings, sports and pop-culture references—which were unfamiliar to her—were often used as analogies and in examples when teaching important topics. Prior to the easy accessibility of GenAI, Shira may have just accepted this struggle as part of her learning journey. Possibly, she may have tried to overcome this roadblock by seeking additional help from an instructor or a peer, or by using the internet to seek alternatives. Now, to help her continue her learning journey in the moment, Shira leverages GenAI by asking it to clarify the framing of specific difficult concepts and by creating unique examples more personalized and relevant to her. 

Sample Shira Prompt: 

I’m taking a data science course that uses baseball as an example to explain statistical analysis concepts. Can you help me develop an example that is more familiar and relevant to me – perhaps using data around technology in Kenya or the cultural differences I’ve experienced since moving to the US?

Opportunities

💡As an educator, you might not be able to create personalized examples for every student. You can leverage GenAI to augment your teaching by:

  • Promoting the creative application of GenAI to create unique examples
  • Sharing similar prompts among learners to foster creative thinking
  • Collecting and promoting unique learner-generated examples, to help foster a collaborative learning environment that promotes the ethical use of GenAI

Learning risks to consider: 

  • GenAI may offer irrelevant or misleading examples
  • Propagation of inaccurate, outdated, or tangential information
  • Misguided focus away from crucial course content

How to navigate these risks:

  • Educate students about the risks of using GenAI and emphasize the importance of validating information. 
  • Review the examples generated by learners to ensure relevance and to correct any misleading or irrelevant information.
  • Encourage students to use the tool as a supplement to the course, not a replacement.
  • Provide clear guidelines about effectively using GenAI.

Learner Case Study 2

How Zhehar Got Unblocked on Assessment Instructions

Problem: Difficulty interpreting assessment instructions and expectations

Zhehar’s Story: We heard from Zhehar, a learner with limited English proficiency who struggled with interpreting assessment instructions and expectations. Prior to the easy availability of GenAI tools, Zhehar may have sought clarification with an instructor or simply submitted the assessment using his limited understanding of the instructions. With ChatGPT, Zhehar does not need to wait for clarification. He simply isolates particular components of assessment instructions as an input for ChatGPT and can get alternative explanations and personalized guidance to help him understand what is being asked and what is expected. In one specific case, Zhehar used ChatGPT to break down assessment instructions into a more granular, step-by-step process.  

With ChatGPT, Zhehar would paste part of the assessment instructions and ask:

Sample Zhehar Prompt: 

”Please explain [to] me this sentence, what to do. How do I do a user research study. Explain it to me  step by step  so I  would  complete the task.”

Opportunities

💡As an educator, understanding this use case can significantly enhance your teaching approach—especially in a multicultural, multilingual class. Here, GenAI can provide meaningful support where it might be challenging to craft personalized, step-by-step instructions for every student. Opportunities to promote this strategy in your teaching include:

  • Sharing successful prompts among learners to foster personalized learning
  • Collecting and sharing learner breakdowns of assessment instructions to promote knowledge sharing
  • Using learner-generated instructions to inform and optimize how you refine future assessment instructions and questions 

Learning risks to consider: 

  • Incorrect or incomplete instructions from GenAI due to lack of specific context
  • Over-reliance on GenAI can interfere with development of essential critical thinking and problem-solving skills 
  • Misinterpretation of GenAI-generated instructions
  • Simplifying instructions may remove crucial information and relevant nuance

How to navigate these risks:

  • Regularly review and revise assessments to ensure clear, detailed instructions.
  • Promote independent understanding of instructions and problem-solving prior to using GenAI.
  • Encourage learners to cross-reference the simplified instructions with the original instructions.

Learner Case Study 3

How Jake Generated Targeted Practice Opportunities Tailored to His Needs

Problem: Lack of targeted practice opportunities and extra challenges as an advanced learner  

Jake’s Story: A highly motivated Coursera learner, Jake loves to learn through practice and extend his learning through more challenging problem types. Prior to the release of free GenAI tools, Jake was limited to the formative opportunities offered within his course. He lacked  access to extra practice on particularly challenging topics and to extension activities. During our research, Jake shared that in a course he was taking, he sought additional  practice on very specific topics and more avenues to test and extend his knowledge and skills. To ensure that he was focusing on the right areas and keeping himself engaged, Jake leveraged GenAI to create topic-specific practice opportunities—including questions that extend beyond what the course offered him. This helped Jake maintain his motivation, consolidate his knowledge, sharpen his skills, and push the limits of his skill mastery. 

For example, it was important to Jake to create practice resources, such as extra questions tailored to his performance on specific quiz questions from his accounting class, to help him identify and improve upon gaps in his knowledge as shown below:

Sample Jake Prompt:

 Generate  10  challenging  practice  questions  based on  my  initial  quiz results  that help  me  address the gaps highlighted from  my  quiz results.” 

Opportunities:

💡 As an educator, it is challenging to create extensive practice assessments, tailored to each learner’s needs. From our learnings on how learners are using GenAI to create personalized practice, you can augment your teaching by: 

  • Leveraging GenAI as a tool to create additional practice quiz questions within your own course
  • Teaching students how to use GenAI to augment their learning and self-assessment
  • Collecting and sharing learner-generated extra practice with all learners

Learning risks to consider: 

  • Creating incorrect practice questions or answers
  • Assessing unrelated skills and knowledge
  • Inaccurate or out-of-date information
  • Improper emphasis on less relevant topics

How to navigate these risks:

  • Regularly review practice scenarios generated by AI for relevance, accuracy, and alignment with the course content.
  • Iterate on courses by adding extra practice opportunities.
  • Encourage learners to develop problem-solving skills by creating an effective mix of AI-generated and independent practice opportunities.
  • Inform learners of the associated risks. 

Learner Case Study 4

How Yuki Brainstormed a Starting Point to Help Refine Her Thinking

Problem: Trouble generating and refining ideas without a thought partner

Yuki’s Story: Yuki was taking a course on cybersecurity and considered herself a relative novice learner on the topic as well as new to online learning. She was struggling to come up with an interesting idea for a portfolio assessment and needed support to help her get beyond the first task of the assessment. Yuki turned to GenAI, engaging in a conversation to help brainstorm some initial ideas and then refine her thinking. She found that being able to interact in an active conversational manner was crucial to help her kickstart her thinking and allowed her more time to focus on the rest of the assessment.

With ChatGPT, Yuki would ask ChatGPT:

Sample Yuki Prompt:

“I’m having a hard time figuring out what to focus on for my cybersecurity portfolio assessment. Since I’m relatively new to this field, I’m not sure where to start. Can you help me brainstorm some intriguing project ideas that a novice can tackle effectively?”

Opportunities

As an educator, you can use Yuki’s example to support learning by:

  • Sharing successful prompts among learners to foster active learning
  • Collecting and sharing interesting and relevant learner-generated ideas
  • Demonstrating how to use GenAI in an effective, ethical way to support active learning

Learning risks to consider: 

  • Over-reliance on GenAI, inhibiting independent creative thinking
  • Reframing or slightly modifying existing ideas instead of genuine innovation
  • Misaligned guidance due to lack of context or improper prompting

How to navigate these risks:

  • Encourage the right balance between GenAI use and independent efforts. For example, by setting expectations around generating a specific number of independent ideas before resorting to GenAI assistance.
  • Emphasize the value of independent, creative thinking. 
  • Share tools and resources to help learners with brainstorming and creative thinking.
  • Provide clear guidelines on acceptable and ethical uses of GenAI.

Concluding thoughts

This analysis of Coursera learner case studies reveals a wealth of opportunities to amplify the effectiveness of both teaching and learning . The ways these learners leverage GenAI can help  resolve potential gaps in our current educational system, where it’s hard to attain personalization, especially in larger class sizes. As educators, it’s important to understand the many benefits of GenAI while maintaining sight of potential risks.  We hope that these case studies provide you, as educators, a refined understanding of various GenAI applications and how to leverage it as a tool to augment your teaching and learning. 

Thank you to the Coursera team members who supported this initiative:

Amrita Thakur, Christina Anderson, Chi Wang, Jessica PelletierSara Gabriele, Tatiana Londoño

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