Retrieval Practice is a well-supported, evidence-based technique that involves actively retrieving information from memory to enhance long-term retention. Examples of retrieval practice activities include self-quizzing, practice tests, and creating lists or memory matrices of key concepts.

Retrieval Practice Assignments with AI

• Practice Questions: Have AI create practice questions on topics or notes the student inputs, then answer without using notes. Instruct students to prompt it to include various question types, from multiple choice to open-ended. After answering, the AI can provide feedback on the quality of their responses and create a summary of their learning gaps.
• Incomplete Memory Matrix: Have students input a topic into an AI tool and generate an incomplete memory matrix or concept map. Students should then complete the visual representation from memory, without referring to their notes.

Dual Coding combines words and visuals to improve learning by processing of information through verbal and visual pathways.

Dual Coding Assignments with AI

• Argument Maps: Instruct students to input their arguments on a topic into an AI tool and request the AI to create an argument map—a visual representation showing how premises are linked. Students should reflect on how the visual format helped them identify any missing or incomplete premises and then revise their argument accordingly.
• Concept Map Comparisons: Have students create their own concept maps on a topic using course readings or other textual materials. Then, ask AI to generate a concept map of the same material. Students should compare the two maps, analyze the strengths and weaknesses of each, and reflect on how the visual representations aided their understanding.

Elaboration is a strategy that involves linking new information to existing knowledge and adding details to reinforce understanding. This approach helps create meaningful connections that make new information easier to retain.

Elaboration Assignments with AI

• Student created analogies or examples: Have students list topics or concepts they are studying and use AI to prompt them to generate contextual examples, analogies, or illustrations to explain these concepts. Students should teach these ideas to the AI, which can ask for further elaboration or clarification.
• Augment the AI’s Arguments: Assign students to use AI to generate arguments on multiple sides of a debate topic related to the course. Students should then elaborate on these arguments, adding evidence, examples, and connections to course concepts. Have students share the initial arguments from the AI alongside their additional points and examples.

Interleaving is a learning strategy that involves mixing different types of tasks, topics, or skills during study sessions rather than focusing on one topic at a time. This approach reduces interference between tasks and enhances long-term retention. For instance, in basketball, players who practice a variety of shots interleaved with each other develop better accuracy than those who practice the same shot repeatedly.

Interleaving Assignments with AI

• Practice Quizzes with Multiple Topics: Have students compile a list of topics or concepts, or upload notes on various subjects to the AI, and then ask it to create a practice quiz that interleaves different question topics and types.
• Interleaving in Case Studies: Assign students to use AI to create a case study that integrates multiple processes or concepts. The AI-generated case should prompt students with questions that require them to apply and connect distinct areas of knowledge.