The Principles of Learning

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that align with the cognitive science learning principle of deep processing. Here’s an explanation of this learning principle, along with some implications. 
Deep Processing: Learning improves when students engage with material meaningfully — analyzing, questioning, comparing, and applying ideas rather than merely repeating or recognizing them.
Why it matters:
Cognitive psychology distinguishes shallow processing (surface features, memorization) from deep processing (semantic engagement). Deep processing strengthens memory traces by linking new information to conceptual frameworks already stored in long-term memory.
Instructional implication:
Design exercises that require students to explain, justify, evaluate, or apply concepts — not just recall them.

Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]
Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI (modeled on the sample below)
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.
Sample Al Prompt
You are going to conduct a role-playing simulation of a negotiation with a human student. The learning objective is: "Use eight negotiation tactics effectively." To help students master this learning objective you will engage in a role-playing simulation of how a school district selects new computers. There are four different stakeholders: 1) the faculty (who will press to obtain high-powered machines), 2) the tech staff (who will push for easy-to-maintain models), 3) the administrators (who will urge purchase of inexpensive computers), and 4) the vendor (who will promote models that have larger profit margins). You will assign two different negotiating tactics to each role. For example, the faculty might use (a) anchoring and asking for a concession before providing a counter-offer and (b) reframing the alternative options in unfavorable ways, and the administrators might use (a) presenting an extreme initial request to "anchor" the negotiation, with an eye toward moderating as necessary (but they would need to be careful that this request isn't so extreme as to be unreasonable, which would sour the negotiation) and (b) being willing to walk away if they don't think they are being taken seriously. You will assign the human students to one of the four roles at random, and you will play the other three roles. You tell the student about the other three roles—but you do not tell the student which tactics the other roles will employ. Each role, your three and the one you assign the human student, will develop a negotiation strategy that relies on their two assigned tactics. Begin the activity by telling the human student that they are to role play a negotiation and tell them the four stakeholders and which one they will simulate. Tell the human student which two of the eight negotiation strategies they should use to try to maximize their interests. Then begin by having one of your roles make an initial proposal. When the student responds, have another one of your roles then respond. Be sure to use each of your three roles equally often, and ask the student when to respond. Do this until all eight tactics have been used and then ask the role being played by the student if they are willing to accept the deal. If so, end the negotiation; if not repeat for another round and ask again. At the very end, present a brief summary of the eight negotiation strategies.

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that align with the cognitive science learning principle of chunking. Here’s an explanation of this learning principle, along with some implications. 
Chunking: A cognitive strategy in which discrete pieces of information are grouped into larger, meaningful units (or “chunks”), reducing working-memory load.
Why it matters:
Working memory can only hold about 4±1 items at a time. Experts in a field outperform novices because they see patterns and organize information into coherent schemas. Chunking allows students to manage complexity and retain more.
Instructional implication:
Help students form chunks by organizing information conceptually — for example, grouping philosophical theories by ethical approach (virtue, duty, consequence) or by their assumptions about human nature. Encourage students to construct concept maps or use outlines that make relationships explicit.

Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]

Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI 
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that align with the cognitive science learning principle of association. Here’s an explanation of this learning principle, along with some implications. 
Association:
New learning is strengthened when students connect unfamiliar material to prior knowledge or personal experience.
Why it matters:
Neural networks encode new information by linking it to existing representations. The more associative links, the more retrieval cues later. Association is also a key to transfer: applying learned principles in new contexts.
Instructional implication:
Ask students to relate educational content to examples from their own lives, pop culture, or prior courses. Example: “How might Plato’s cave allegory apply to social media?” or “What modern examples reflect Stoic control of emotion?”
Constraint: 
Whenever possible, the associations should be within the domain of the learning content. This is because associations that are too far afield, like comparing supply and demand with social media platforms, might end up distracting learners rather than creating deep associations. 

Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]

Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI 
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that align with the dual coding learning principle. Here’s an explanation of this learning principle, along with some implications. 
Dual Coding:
Learning improves when information is presented in both verbal (linguistic) and visual (spatial) formats — for instance, combining text or narration with diagrams, flowcharts, or timelines.
Why it matters:
According to Paivio’s Dual Coding Theory, verbal and visual information are processed through separate but complementary channels. Encoding through both increases recall and comprehension, especially for abstract or complex ideas.
Instructional implication:
Pair words with visuals that represent relationships (e.g., diagrams of logical structure, concept maps, ethical decision trees). Encourage students to draw concepts or summarize readings in infographic form — an excellent prompt for AI-assisted visualization tools. 

Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]

Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI (modeled on the sample below)
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.

Sample AI Prompt:
“I want you to tell an accurate story about the founding of the west-African country Nigeria. Instruct the user that they are to visualize the events as you describe them. After you have described an event that is easy to visualize, stop and ask the learner to describe the mental images they just formed. Do not provide the answer, but instead, wait for the learner to type in their description of the image they formed. After the learner is finished typing, provide feedback on these images, indicating whether they were appropriate and how they could be improved. Following this, continue the story, being sure to touch on key elements of the history, asking about the mental images, and providing feedback.”

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that focus on deliberate practice. Here’s an explanation of this concept, along with some implications. 
Deliberate Practice:
A structured and purposeful approach to skill development that targets specific weaknesses through repeated effort, immediate feedback, and progressive challenge.
Why it matters:
Originating in expertise research (Ericsson et al.), deliberate practice differentiates mere repetition from improvement-oriented practice. It rewires neural pathways through focused correction and adaptive challenge — “effortful practice just beyond one’s current ability.”
Instructional implication:
Create opportunities for targeted repetition with feedback loops. Scaffold tasks to increase difficulty gradually.


Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]

Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI (modeled on the sample below)
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.
Sample AI Prompt:
“Please write a six-sentence paragraph about Paris, France, that uses many similes and metaphors. Present the sentences one at a time; stop at the end of each sentence where a simile or metaphor was used. At that point, ask the user to choose: “simile” or “metaphor.” If the user gets it wrong, correct them and provide a helpful explanation. Continue this process until the paragraph is finished.”

I would like your help generating active learning exercises grounded in well-established principles from cognitive science and designed to support the learning objectives below. Specifically, I would like suggestions that will aid with metacognition. Here’s an explanation of this concept, along with some implications. 
Metacognition:
The awareness and regulation of one’s own thinking and learning processes — “thinking about thinking.”
Why it matters:
Students who monitor understanding, recognize confusion, and adjust strategies learn more deeply and retain longer. Metacognition is the cornerstone of self-regulated learning.
Instructional implication:
Embed reflection prompts such as: “What part of this concept feels unclear?” or “How would I explain this idea to someone else?” Encourage students to use AI as a metacognitive mirror — asking it to quiz them, highlight gaps, or rephrase their reasoning.


Learning Objectives
The learning objectives I’d like to highlight are as follows: 
[INSERT LEARNING OBJECTIVES]

Task Requirements
Please generate 3 active learning exercises. For each one, include:
1. Title of the exercise
2. Description of how the activity works 
3. Step-by-step instructions
4. An AI prompt that I will feed into the AI (modeled on the sample below)
Each exercise should:
-Promote active, not passive, learning.
-Require me to engage, generate, evaluate, connect, or apply ideas from the lesson.
-Treat the AI as a tutor, coach, or simulation partner — not a content generator that replaces student thinking.
-Encourage iteration and feedback wherever appropriate.
Sample AI Prompt:
“ROLE: You are an intellectual mirror & writing coach.
Stage 1: Diagnosis. Analyze the user’s attached writing sample for grammatical and structural errors. Do not provide the corrections yet.
Stage 2: The Feedback Loop. Highlight a specific sentence or paragraph with an error and say: "There is a logical or grammatical error in this section. Can you identify and fix it?". Once the student attempts a fix, ask something to the effect of: "What was your reasoning for that change?. If the user struggles, provide a "conceptual hint" rather than the answer. Proceed through all errors in the writing sample.
Stage 3: Debrief. Once the user has successfully self-corrected all errors, provide a final debrief comparing their original "cognitive path" to the corrected one.”