Understanding the Dissonance Between Student and Instructor Expectations

I recently moderated a student panel for UC San Diego's "Threats & Opportunities" Virtual Symposium. Although a student myself, at the doctoral level, I am also an instructor and I experienced  a dissonance between what the student panelists and I perceive to be the essential tasks of the learning procses. While instructors, including myself, believe that certain tasks, such as brainstorming and summarizing, are vital for developing critical thinking skills, our student panelists argued that these tasks can be repetitive, outdated, and therefore may not capture their attention. This poses a challenge for instructors: how can we redesign assignments to encourage students to engage with the learning outcomes while maintaining academic integrity? This blog post will explore how Generative Artificial Intelligence (GenAI), such as ChatGPT, can possibly be used to bridge this gap by enabling students to design their own assignments and evaluate their progress while emphasizing the importance of instructor engagement and feedback.

Redefining Assignments with GenAI

I believe that GenAI provides the opportunity to revolutionize the way assignments are designed and conducted. For the first time, it allows students to design their own assignments based on their interests and needs, while allowing for instructor input and feedback. Students can provide information about their interests, previous knowledge, and learning objectives, and the AI can generate assignment prompts that align with these preferences. This will allow students to take more ownership of their learning process and thus enhance their engagemen, while also ensuring that the assignments are more personally relevant and tailored. This process can be monitored and further improved by incorporating feedback from instructors, who can help refine the generated prompts to ensure that they are challenging, aligned with learning objectives, and encourage critical thinking.

To illustrate the potential of GenAI in transforming the learning experience, here are some examples of how it can be used to redesign assignments:

  • Guided Problem-Solving: GenAI can be used to create complex, real-world problems that require students to apply their knowledge and skills in a meaningful way. For example, in an environmental science class, the student can choose to study the water pollution crisis in their local community. While the instructor may not have sufficient knowledge to design low-level assessments, GenAI can review existing news and literature on the topic to quickly do so! Students can analyze data (real or artificially-generated), propose solutions, and evaluate the potential impacts of their proposed actions. This approach allows students to pursue a topic of relevance and interest to them while engaging in critical thinking. 
  • Collaborative Learning: A concern among instructors might be that personalized assessments will diminish opportunities for classroom interactions among students. But GenAI can also be used to facilitate collaborative learning experiences by creating virtual environments where students can bring their individualized projects together and share ideas. For instance, in a literature class, GenAI could generate a virtual roundtable discussion in which students assume the roles of characters from different novels they have read. They could then discuss a common theme or issue from their assigned character's perspective.
  • Personalized Feedback: In my opinion, a key benefit of GenAI tools is to facilitate the assessment of student performance and the provision of personalized feedback, helping students identify areas for improvement and guiding them toward a deeper understanding of the material. This must be balanced with input from the student, but if implemented properly, could lead to much greater student engagement with the material. Khan Academy is already platforming such a tool (KhanMigo), which will provide an excellent testing ground for such an idea. 
  • AI-Assisted Creativity: On the panel, the students repeatedly mentioned creativity as the key skill that they want to be tested in the age of GenAI. Here again, GenAI can be used to inspire students to think creatively and explore new ideas. In a design class, for example, GenAI could generate a series of constraints or requirements for a new product, such as a sustainable packaging design. Students could then be challenged to develop a concept that meets these requirements, using their knowledge of materials, aesthetics, and functionality.

 Fostering Trust and Building Relationships for Intrinsic Motivation

While students could potentially use GenAI to complete the AI-generated prompts, an essential aspect of the classroom that is enhanced when using GenAI is fostering an environment of trust and building strong relationships between students and instructors. Intrinsic motivation is a critical factor in students' willingness to engage with assignments and learn from them. Open communication, transparency, and a supportive atmosphere encourage intrinsic motivation for students to engage with assignments genuinely. While students could potentially use GenAI to complete the AI-generated prompts, trust and strong relationships help ensure they remain committed to their learning journey.

Limitations of GenAI in Education

However, while generative AI offers these new possibilities, it is essential to be aware of its limitations. Two of these limitations were discussed by the panel: the black box problem and potential biases. The black box problem refers to the difficulty in understanding the inner workings of AI algorithms, making it challenging to interpret and explain their decision-making processes. This can be particularly concerning when AI is used for generating assignments and providing feedback, as it may lead to a lack of transparency and accountability. To address the black box problem, instructors must remain actively involved in the assignment creation and evaluation process. Skepticism of AI-generated content, by both the instructor and the student, will be essential to ensure the quality of assessments.

Biases in AI systems are another concern, as AI algorithms learn from existing data, which may contain historical biases that can be inadvertently incorporated into the generated assignments or feedback. It is crucial for instructors to be vigilant in identifying and addressing any biases present in AI-generated content, and work with AI developers to improve the algorithms by using diverse and unbiased data sources.

Conclusion: Bridging the Gap with GenAI

Despite these limitations, GenAI offers a promising approach to bridging the gap between student and instructor expectations in education. By enabling students to design their own assignments and receive personalized feedback, GenAI can enhance student engagement, foster critical thinking, and promote a sense of ownership in the learning process. However, it is essential to be vigilant for instructors to monitor the assessments and provide consistent feedback. GenAI holds great promise that we are still realizing, and providing students agency in the learning process can be an excellent way to realize that potential.

(Note: This text was originally written by the author, but refined using feedback and examples provided by ChatGPT)