Rethinking AIs Role in Education - From Lesson Plans to Learning Frameworks

The promise of AI in education can be undeniably alluring. Imagine: with a few simple inputs – subject, learning area, desired outcomes – a single button press generates a comprehensive lesson plan. Whether through a conversational exchange with ChatGPT or the click of a dedicated AI tool, the speed at which educators can now produce these plans seems 'revolutionary', promising to "save valuable time" as the marketing hype often proclaims.

As a specialist in alternate learning methods, such as Project-Based Learning (PBL), my interest lies in exploring how AI can genuinely enhance the learning experience.

This post is inspired by a recent post by Dr Philippa Hardman , a true pioneer in this space. Dr Hardman discusses how 美国亚利桑那州立大学 (ASU) is utilising AI, specifically through a partnership with OpenAI , to reshape education. ASU crowdsourced ideas from its community, resulting in numerous projects that use AI to personalise learning, support research, and enhance future work skills.

She then analyses the impact of these AI initiatives on the role of instructional designers at ASU, highlighting how tasks across the ADDIE model (analysis, design, development, implementation, and evaluation) are evolving. It emphasises the emerging skills needed for instructional designers in the age of AI, such as AI literacy, data analysis, and ethical AI use, concluding that AI will ultimately elevate, not eliminate, the importance of instructional design.

Three key themes noticed in ASU's prioritised projects:

Student-focused: Most projects are designed to directly benefit student learning.

Personalisation: Many projects are designed to offer personalised learning experiences, typically through the provision of 1:1 support using AI.

Real-world skill development: Several projects focus on practical skills needed in professional settings.

Two parts of the article struck me the most:

AI Literacy

We are seeing a shift from IDs (Instructional Designers) needing basic digital literacy skills to them requiring more in-depth understanding of AI capabilities and limitations. In practice, IDs need to understand different types of AI (e.g., natural language processing, machine learning), their applications in education, and how to effectively integrate them into course design.

Prompt Engineering

At least in the immediate term, IDs need to hone their prompt engineering skills to ensure that they are able to guide AI in generating relevant, accurate, and pedagogically sound content and assessments. Check out this post by Dr Hardman on how to 10X your prompting skills in 10 mins .

The Extended Path to AI Proficiency: Transforming Educational Paradigms

I've observed a recurring theme in numerous webinars and workshops, leaving me to ponder its implications. "Witness the capabilities of AI," the presenter proclaims, as they navigate a platform and trigger a button to generate a lesson or a set of slides. "Consider the time you'll conserve!" With a few inputs like subject, area, and learning objectives, a simple button can yield an elaborate lesson plan. Lack the time to create professional development? Why not delegate that task to AI? Do we ever pause to reflect on the message this conveys to others? What about parents? Imagine if they perceived you as merely lounging, watching Netflix, and intermittently pressing buttons on your phone during snack breaks instead of focusing on the students.

The Lengthy Journey to AI Fluency: Reimagining Learning Frameworks

Imagine if, just like a young Leonardo Da Vinci meticulously studying the flight of birds, we as educators could dissect the very essence of learning itself. Contemplate a critical shift in educational thinking: transitioning from incorporating AI to just create lesson plans to integrating AI into the bedrock of our teaching - the learning frameworks themselves. What if, instead of utilising AI to 'save time,' we harness it to enrich multiple facets of the learning experience itself?

This transformation begins with us.

Initial Exploration: Understanding the Terrain

Start with the student. What even constitutes personalised learning, anyway? Is it even feasible for every student to have their own learning plan? What about their own lesson? Let's consider a grade 5 student with a simple calculation using AI:

Lessons for one student: Let's assume a grade 5 student has 5 different classes each day (such as Math, Science, Reading, etc.). This implies the teacher would need to create 5 personalised lessons for that single student every day.

Considering a whole school week (let's say 5 days), the teacher would need to make 5 lessons per day x 5 days a week = 25 personalised lessons for that one student each week. They would also have to find ways to monitor those lessons to ensure they remain at the right level and meet the student's needs.

Lessons for the whole class: Now, let's envision there are 20 students in the class. The teacher would then need to create 25 lessons per student x 20 students = 500 personalised lessons every week for the entire class!

Can a button solve that? The integration of AI into learning frameworks extends far beyond generating 500 lesson plans. A teacher pressing a button 500 times a week even sounds crazy. If that is meant to save time, then I'm confused.

Unpacking the Core Concepts

What is a Learning Framework?

A learning framework, in its essence, is the blueprint upon which educational experiences are built. It outlines the pedagogical principles, instructional strategies, and assessment methods that guide the learning process. A learning framework is not a rigid set of rules, but rather a dynamic structure that can be adapted to suit diverse learning needs and contexts.

Example: Dissecting Project-Based Learning (PBL)

PBL exemplifies a robust learning framework. It emphasises student-centred learning, active inquiry, and real-world application. For me, it is the only framework that can truly integrate Sustainability, due to its holistic and real-world nature. PBL immerses students in complex, authentic problems that require them to collaborate (not just be put in teams), research (through reflection and portfolio progression), and develop solutions, prototypes, and have opportunities to showcase the project journey.

Why Consider Learning Frameworks at All?

Learning frameworks provide educators with a cohesive and intentional approach to teaching. They offer a structure for designing engaging learning experiences that promote deep understanding, critical thinking, and creativity. As Grant Wiggins and Jay McTighe advocate in their Understanding by Design framework, "Begin with the end in mind." By starting with a clear learning framework, educators can ensure that their instruction is aligned with desired learning outcomes.

The Button Analogy: Lesson Plans vs. Frameworks

Imagine two buttons. One generates a detailed lesson plan on a specific topic. The other generates a customised learning framework based on your pedagogical goals, student needs, and available resources. The first button offers a quick solution, but the second empowers you to design a more impactful and sustainable learning experience.

The lack of a "framework generation" button emphasises the complexity of designing effective learning frameworks. It requires in-depth pedagogical knowledge, careful consideration of student needs, and a nuanced understanding of how AI can enhance, not replace, human interaction.

It's worth noting that I've spent well over a year 'developing' a PBL framework button. It's evolved into multiple 'buttons' and a framework behind those 'buttons'...

Implications for Teachers and Leaders

• Collaborative Curriculum Design: I suggest teachers work together to understand and develop new learning frameworks that incorporate AI tools in meaningful ways.
• Professional Development: Schools and districts should provide ongoing professional development for educators on the ethical and pedagogical use of AI in education, and explore creating school learning frameworks that meet the needs of the school community and district.
• Student Agency: We have a chance to get this right from the start, not just a tacky 'student involvement' addon. Involve students in co-creating a learning framework. AIxPBL is developed from many implementations, student feedback, understandings and success.

Conclusion: Rethinking AIs Role in Education - From Lesson Plans to Learning Frameworks

This exploration has highlighted a critical shift in thinking about AI's role in education. While the allure of AI-generated lesson plans is undeniable, a more impactful approach lies in leveraging AI to enhance and personalise the foundations of our teaching – our learning frameworks.

Imagine a future where, instead of a "lesson plan" button, we have a "framework generator" at our disposal. This generator, informed by our pedagogical goals, student needs, subject area, and specific challenges (like engagement or complexity), would suggest the most suitable learning framework and provide tailored guidance for its implementation. This approach empowers educators to move beyond isolated lessons and embrace a more holistic, student-centered approach to learning.

Interested in Understanding Learning Frameworks?

To embark on this journey, educators can investigate and adapt a variety of established learning frameworks, potentially enhanced by AI insights:

10 Learning Frameworks for Exploration:

1. Project-Based Learning (PBL): Fosters deep learning through real-world projects and student-driven inquiry.
2. Inquiry-Based Learning: Encourages students to ask questions, investigate, and construct their own understanding.
3. Universal Design for Learning (UDL): Creates flexible learning environments that cater to diverse learning styles and needs.
4. Flipped Classroom: Reverses traditional teaching, with students engaging with content at home and applying it in class.
5. Game-Based Learning: Leverages the power of games to motivate and engage students in learning.
6. Mastery Learning: Ensures students master key concepts before moving on to new material.
7. Problem-Based Learning: Similar to PBL, but focuses on solving ill-defined, open-ended problems.
8. Design Thinking: Encourages students to empathise, define problems, ideate solutions, prototype, and test.
9. Backward Design: Starts with the desired outcomes and works backward to plan curriculum and assessments.
10. ADDIE Model: A systematic instructional design process with five phases: Analysis, Design, Development, Implementation, and Evaluation.

For each framework, educators can utilise AI-powered tools to explore its potential through prompts like:

• "Given my goal of [learning goal] in [subject area] and the current challenge of [student engagement/complexity], how can I adapt the [framework name] to address these needs?"
• "What AI tools can support the implementation of the [framework name] in my classroom, specifically for [differentiation/assessment/feedback]?"
• "Can AI help me personalize the [framework name] to cater to the diverse learning styles and needs of my students?"
• "How can I use AI to track student progress and provide targeted support within the [framework name]?"
• "What are the potential ethical considerations of using AI within the [framework name], and how can I mitigate them?"

By shifting our focus from individual lesson plans to the broader frameworks that guide our teaching, we can harness the true potential of AI to revamp education.

Let's get to work

Phil