Designing AI-Enhanced Assignments for Deeper Learning

Student with a laptop engaging in AI-enhanced deeper learning
(Image credit: Pixabay)

Due to the rise of generative AI-powered tools, educators need to consider retooling their assignments to push assignments to the higher levels of Bloom’s taxonomy.

While AI offers undeniable benefits in streamlining processes and providing data-driven insights, concerns remain about its impact on fostering deeper learning experiences. To bridge that gap, educators need to adapt practical strategies to design AI-enhanced assignments that engage students in higher-order thinking.

Creating A Deeper Learning AI Assignment

Generative AI tools can be powerful allies in creating assignments that move beyond basic knowledge recall and organization. One way to achieve this is by utilizing AI to generate large, diverse datasets for students to analyze.

For example, a traditional history class assignment might relate to asking students to create an event timeline. Multiple generative AI tools could create such a timeline. By allowing students to use an AI tool to create such a timeline, they will have to learn to craft an effective prompt.

Sample prompt: Please create a brief timeline of events of the American Revolution that occurred along the Mississippi River.

Then ask the students to evaluate the resulting timeline. In this specific example, the results missed the primary battles of St. Louis and Cahokia fought in May 1780. It additionally missed the Spanish and British events on the lower Mississippi.

Ultimately, this assignment encourages students to access and organize information and then critically analyze the results, drawing connections and making informed inferences, a key component of the "analyze" level in Bloom's taxonomy.

Alternatively, or as a further extension of the assignment, students can be asked to refine their prompts until a more credible timeline is created.

Evaluative Assignments

At the evaluation level, an assignment could be to have students ask an AI tool to draft a version of a ubiquitous five-paragraph essay previously assigned. Then the students can be asked to edit the generated results and justify their edits in a brief essay.

This allows the original five-paragraph essay assignment to be utilized while pushing the exercise further up the Bloom hierarchy.

To help make sure the students are personally engaged and not simply using two generative tools, embed the need for students to cite their personal experiences within the scope of their feedback. Requiring students to relate assignments to personal experiences or local environments are two good ways to defeat generative AI tools, which still struggle with personalization at this point.

While these examples focus on generative AI for enhancing learning, it's crucial to acknowledge potential concerns. Over-reliance on AI for assessments can detract from the value of human expertise. It's important to remember that AI serves as a tool to augment, not replace, the role of instructors. Educators must carefully select appropriate tools and provide context and guidance for student engagement with generative AI.

By embracing a considered approach to the integration of generative AI, educators can create a dynamic learning environment that fosters deeper intellectual inquiry. AI-enhanced assignments can empower students to move beyond memorization and develop critical thinking and evaluative skills, hallmarks of the upper levels of Bloom's taxonomy. This approach ultimately paves the way for a more engaging and enriching learning experience for students.

Steve Baule served as a technology director, high school principal, and superintendent for 20+ years in K-12 education. He is currently the director of Winona State University’s online educational doctorate program in Minnesota.