Welcome to the support page for my workshop session, Lassoing AI for Learning: High-Impact Strategies That Work. The session description, slide deck, game cards, and other resources are available in this Google Drive-hosted, Resources folder. All items are freely shared under CC-BY-SA copyright.

Short URL – https://go.mgpd.org/lassoAI

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Menu: Slide Deck | Padlet | ALDO | Strategies by Phase of Learning | SOLO | Activity

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Note: I relied on Google Gemini, Perplexity, and ChatGPT to generate various aspects of the activities below then modified them. I also had great assistance from my colleagues, Peggy R., Diana B., and Simba M. My thanks to them!

Slide Deck

The slide deck is available in Canva for viewing (updated). You can get a PDF/PPTx version in the resources folder. You can also play the High Yield Strategy Derby game if you have a free BoodleBox account (or get one via instructions here).

Padlet Activity

The link to the Padlet is not provided here (just in case). It can be found in the slide deck, however.

Amazing Lesson Design Outline (ALDO)

Learn more about ALDO via the TCEA TechNotes blog entry.

Some folks rely on ALDO as a tool to facilitate instructional coaching conversations with classroom teachers. Others use it as a set of reminders when designing classroom instruction.

Strategies by Phase of Learning

You can organize high-effect size instructional strategies into various phases of learning students may be in, or your intended learning intentions. Here are some of my favorites in the table below.

Surface Learning	Deep Learning	Transfer Learning
Introduce skills, concepts, strategies, building requisite knowledge. (Pre/Uni-structural)	Build relationships between ideas, concepts. Assist students in gaining deeper, conceptual understanding.(Multi-Structural, Relational)	Apply what has been learned to novel situations that may involve metacognition, collaboration, and problem-solving. (Extended Abstract)
Jigsaw Method (0.92)	Jigsaw Method (0.92)	Self-Reported Grades (0.96)
Feedback: Corrective, reinforcement & cues (0.92)	Argumentation (.86)	Integrate with Prior Knowledge (.93)
Repeated Reading Programs (.80)	Self-Judgement and Reflection (.81)	Transfer Strategies (.75)
Mnemonics (.65)	Elaboration and organization (.72)	Self-efficacy (.71)
Vocabulary Programs (.65)	Reciprocal Teaching (.74)	Problem-Solving Teaching (.61)
Flipped Classroom (.58)	Outlining and Organizing (.86)	Service Learning (.53)
Direct Instruction (.56)	Concept Mapping (.66)	Peer tutoring (.66)
Questioning (.49)	Metacognition Strategies (.58)	Cooperative learning (.55)

The SOLO Taxonomy and AI

“The greatest value of SOLO is that it shows how our learning develops,” says Geoff Petty in his book, Evidence-Based Teaching (a must-read and must-have reference). I wrote a blog entry explaining this. There’s more work that could be done, but I like Professor John Bigg’s SOLO Taxonomy as a guide over Bloom’s Taxonomy or other similar tools.

Activity: Lasso the Right Tool

This is not an exhaustive list of available strategies, only some you might adapt for use with a variety of AI tools. Remember, rely on SOLO Taxonomy to guide your use of GenAI, as well as supervised student use of AI in K-12. Ask yourself, “Will AI use interfere with productive struggle needed for learning?”

Placement on Trail Map (SOLO Phase)	Horse Card (AI Tool Application & Function)	Rider Card (Strategy, Effect Size & Focus)	Rationale
Surface Learning	AI-Powered Quizmaster Function: Generates open-ended questions about a topic that require students to recall answers from memory, without multiple-choice cues.	Retrieval Practice Effect Size: 0.46 Focus: Actively recalling prior knowledge to strengthen memory and identify gaps.	Automates the creation of low-stakes quizzes that force active recall, a process proven to strengthen long-term memory for foundational knowledge.
Surface Learning	AI as a Creative Memory Aid Generator Function: Provide the AI with a list of terms. Ask it to generate creative mnemonic devices like acronyms or memorable sentences to aid recall.	Mnemonics Effect Size: 0.65 Focus: Using memory aids to recall facts and lists for building requisite knowledge.	The AI quickly generates creative and effective memory aids, helping students efficiently lock in foundational facts and lists, which is a key goal of surface learning.
Surface Learning	AI as a Concept Clarifier Function: Generates student-friendly definitions, examples, non-examples, and an image to represent a new vocabulary term.	Vocabulary Programs Effect Size: 0.65 Focus: Introduce skills and concepts by building requisite knowledge.	AI quickly provides multiple representations for new words, helping students build foundational knowledge efficiently and in a culturally responsive manner. Beware of bias.
Surface / Deep Learning	AI as a Content Differentiator Function: Ask the AI to generate reading passages on a single topic but at different complexity levels for the “expert” groups.	Jigsaw Method Effect Size: 0.92 Focus: Introducing new skills and concepts through peer teaching and collaborative learning.	The AI streamlines the most time-consuming part of Jigsaw—sourcing differentiated texts. This allows the teacher to focus on facilitating the peer-to-peer learning where students build both surface knowledge and deeper connections.
Surface / Deep Learning	AI-Powered Inquiry Starter Function: Generates different levels of questions (e.g., based on SOLO Taxonomy) from a text or topic to spark discussion.	Questioning Effect Size: 0.49 Focus: Assess prior knowledge and build conceptual understanding by exploring a topic.	AI automates the creation of leveled questions, allowing teachers to easily facilitate deeper inquiry and check for understanding at multiple levels.
Surface / Deep Learning	AI as an “Error Analyst” Function: Analyze a student’s answer to a problem, identify the specific error, and provide a targeted hint or a similar practice problem, without giving away the final answer.	Corrective Feedback Effect Size: 0.92 Focus: Providing specific information to students about what is correct and incorrect to guide improvement.	The AI can provide immediate, individualized corrective feedback at scale, allowing students to fix misconceptions in the moment and freeing the teacher to focus on more complex needs.
Deep Learning	AI as a “Summarization Coach” Function: A student writes a summary, and the AI analyzes it against the original source, providing feedback and guiding questions to help the student improve it.	Summarization Effect Size: 0.74 Focus: Building deeper conceptual understanding by identifying and connecting key ideas.	This process is metacognitive. The student evaluates their own understanding with scaffolded support, pushing them to think critically about the text’s structure and meaning to build deeper comprehension.
Deep Learning	AI as a “Digital Teammate” Function: Act as a “student” in a digital group, taking on one of the reciprocal teaching roles (e.g., Questioner, Clarifier) to model the process or fill in for a student.	Reciprocal Teaching Effect Size: 0.74 Focus: Build relationships between ideas through collaborative dialogue about a text.	The AI provides a consistent model for the targeted skill, scaffolds the conversation, and ensures the cognitive lift remains with the students as they lead the discussion.
Deep Learning	AI as an Idea Connector Function: The AI generates a draft concept map. The student’s task is to then critique, correct, and expand upon the AI-generated map, justifying their changes.	Concept Mapping Effect Size: 0.66 Focus: Building relationships between ideas to gain deeper, conceptual understanding.	The core cognitive work of validating and deepening the connections between ideas remains with the student. This process of analyzing and refining relationships is a key deep learning activity.
Deep Learning	AI as a “Socratic Debate Partner” Function: The AI takes an opposing viewpoint, challenging the student’s claims, asking for evidence, and identifying logical fallacies in a structured debate.	Argumentation Effect Size: 0.86 Focus: Building relationships between ideas and gaining a deeper conceptual understanding through structured debate.	The AI provides a rigorous and safe environment for students to practice constructing and defending arguments, helping them anticipate counterarguments and deepen their content knowledge.
Deep / Transfer Learning	AI as a “Thought Partner” Function: Engage a student in a dialogue about their learning. Ask reflective questions like, “What strategy did you use and why?” or “What would you do differently next time?”	Metacognition Strategies Effect Size: 0.58 Focus: Assisting students in gaining deeper understanding by reflecting on their own learning process.	The AI acts as a non-judgmental “thought partner,” guiding students to think about their own thinking, which is crucial for developing independent, self-regulated learners.
Transfer Learning	AI as a “Real-World Scenario Designer” Function: The AI generates a complex, multi-step problem and acts as a “consultant” that students can query for data, but it will not provide the solution.	Problem-Solving Teaching Effect Size: 0.68 Focus: Applying what has been learned to novel situations that may involve metacognition and collaboration.	This is the essence of transfer: applying knowledge to a novel context. The AI creates an authentic, ill-structured problem that requires students to strategize and synthesize a solution on their own.