Escaping Powerpoint Purgatory - Breaking the Chalk-Talk-Chain

I’ve been watching with great interest how new AI tools are purporting to be the best new solution for teachers. Teachers show me ‘new and innovative’ ways of working that they have designed in a snap with the support of AI tools without realising that their ‘future lens’ is accidentally facing the wrong way. It seems ironic that we can be on the brink of future-focused learning and have amazing technology to support us to rethink and redesign what training and learning experiences feel like, yet we are accidentally going backwards with more chalk and talk, more snap-made powerpoints and more regurgitative boring experiences.

Soundbytes from the shop floor include: “AI is so great - I don’t even have to think anymore!”, “Chalkie made me this Powerpoint in two minutes”, “I designed a full course in half an hour!” and so on… concerning indeed.

If we think we are innovating but we are still offering teacher-centred experiences tied to the whiteboard and screen - then we are probably not really innovating. Sure, we can make slides in a jiffy - but at what cost?

There has been a lot in the news lately about the cognitive decline caused by out-sourcing our thinking. We know that when AI writes the essay for us, our own understanding of the content has not been changed or challenged. We also know that, just like any muscle, if we don’t use it we lose it. So what does that mean for people who are outsourcing their learning design to AI Powerpoints? It is quick. Yes. But is it good? Is it engaging? Does it really target the productive struggle we all so desperately need to build muscle and master better cognition?

Currently I’m working with a company to overhaul a suite of learning resources for courses and provide contemporary pedagogy support to re-engage learners. What I’m noticing is that the chain to the whiteboard is a safety net that teachers and trainers are reluctant to let go of. When provided with activities that allow learners to ‘go forth and explore’ on their own with guidance from the side, (guide on the side instead of sage on the stage) teachers are using AI to rejig the content back into a Powerpoint so that they can walk them through the content step by step. It’s like presenting learners with a climbing wall but then taking away the hold options and only allowing them to use the easy coloured option. The stretch and challenge of learning is diluted if we insist on staying chained to the whiteboard and offering up Powerpoint purgatory as our learning design solution.

Filling slides with bullet points and text blocks can actually hinder learning. If you reflect on your own experience in board rooms, staff rooms and conferences, how much do you actually take in when presented with a text-heavy slide deck that is read aloud to you? Research shows that it creates cognitive overload and can be disengaging as a consequence. Usually, I read it before the presenter reads it out loud and then I find myself fidgeting with off-task thinking while I wait for others to catch up. Using multiple slides to tell (rather than ‘show’ or ‘offer’) is not an effective solution to engage learners. While it can feel useful from a facilitator’s perspective to have all of the information on the slides in front of us for reference, this is not what learners need. And if we read aloud from the slides, then we may as well tuck them all to bed for a pretty boring bedtime story. Even picture books are more ‘on point’ with less text and more visuals.

AI is being used as another mechanism for reinforcing traditional modes of learning. If we don’t get the basics right and think of shifting pedagogy and really targeting and engineering our prompts to be future-focused and human-centred - then we are going backwards and not forwards. Many modern education solutions are simply reinforcing old habits instead of supporting genuine learning transformation. Chalkie, for example - well - it is in the name. It is definitely another chain we need to break away from.

True innovation is not just about replaceing analog for digital. Yesterday I caught up with an old high school buddy who is a writer and website designer and he said, ‘All the best designers start on paper. If I don’t see a lot of Post-Its and a Sharpie then I know that something is wrong.’ This resonates with me. If we don’t do some messy scribbling and some deliciously ‘what if’ thinking on our own before employing a quick publish with AI, the we may as well let our brains (and our learner’s brains) wither.

AI is trained on historical data that encodes old models of education. Unless we are intentionally prompting with change in mind and intentionally requiring outputs to be experiential/collaborative/future-focused/research-based/aligned to chosen pedagogies - the outputs will favour rote learning and recitation (Powerpoint purgatory). There needs to be a conscious redesign of technology and how we use it as well as a better training datasets for the tools that we employ. Even Microsoft and Google education solutions are so limited in functionality for learner-centred experiences and are proving that developers need to be working alongside serious and experienced educators to understand what modern pedagogy can look like in classrooms and work spaces. Technologies that ‘add sparkle’ by adding emojis to generic templates are frankly useless.

Chalk and talk is a crutch. Some learners need more explanation, some learners need more scaffolding, some learners need more extension opportunities and some learners need a completely different way of engaging with content altogether. This is why Universal Design for Learning or UDL is the chain-breaker that should really be in every carefully engineered prompt (if we are using AI as an assistance tool). Further, if we design learning that is centred around humans in the room being human in the room, then we can guarantee that our brains are the ones doing the work and our brains are flexing and our brains are building muscle.

In ‘Leading Innovative Learning in New Zealand Schools’ (ERO, 2016) nearly ten years ago the recommendation is to prepare learners for a dramatically changing landscape in the future. ‘What is critical is teaching that is personalised and focused on valued [learner] outcomes’ - traditional education and chalk and talk is not the way forward.

I’ve seen firsthand the power of giving learners real world contexts, voice, choice and agency. I’ve seen problem-solving skills increase and creative muscles flexing when provided with interesting challenges to solve in teams. Great learning is not about the technology or the slides - but it is about crafting experiences where learners drive their own journey with dynamic interactions with content as well as people.

Breaking the chalk-talk-chain and escaping Powerpoint purgatory matters because we have to move people from compliance to curiosity. If the world is changing dramatically (and it is), then we need our workforce to be adaptable with curious brains that flex and bend with the tide. Using Powerpoints and a chalking/talking methodology pushes an information pipeline and creates passive recipients. This method of teaching cannot be personalised or individualised and it does not unlock hidden potential or promote true transformation.

We need to embrace a vision for what education can be and work hard to upskill our facilitators to feel confident that they are facing in the right direction by providing experiences that focus on future-durable skills like critical thinking, ethical reasoning, creativity and collaboration among others. AI can be a useful tool in the classroom - but not if it is automating lectures, worksheets and keeping us chained to the whiteboard.

If you’re fed up with tedious solutions, outdated ‘innovation’ or technology that seems to be accidentally facing the wrong way - please get in touch so that I can help you chuck away the chains and propel learning experiences into the future.

Don the black hat! A thinking strategy for doing things better

New Zealand is currently faced with a worrying economic downturn. There are additional threats on the horizon such as the rapid developments of AI, impacts of Globalisation, looming international conflict expansion and environmental disasters due to climate change just to name a few. We turn our faces to the light and try to look for moments of brightness in the news and, particularly in the education sector and public sectors, it feels very dark indeed. Where is the light?

In these situations, it is useful to put on a black hat after Edward De Bono.

What is Black Hat Thinking?

Black hat thinking is a a way to adopt a deliberately pessimistic and cautious approach to problem-solving and decision-making with all of the ‘worstest’ superlative outcomes that we can think of. It means that we get to focus on identifying potential risks, problems, and consider of the possible negative outcomes. It is one of the six thinking styles in Edward de Bono's Six Thinking Hats model.

Why is it useful?
Black hat thinking encourages a kind of healthy skepticism. A lot of technology has been touted as ‘the next best thing’ for example and then it has quickly turned into a bubble that has popped more quickly than we might have predicted. Some examples of these are the dot.com bubble or the open plan classroom bubble - some may say, too, the AI bubble but this has not yet popped. (More on this later). This type of thinking is useful for carefully predicting and examining any potential flaws and weaknesses which ends up being a very useful prediction and forecasting tool.

Logical Analysis
While it might often be perceived as negative and ‘red penning’ any new and exciting prospects, black hat thinking is not about being pessimistic for its own sake. Another way of understanding it is as a useful way to critically analyse new systems/approaches/tools as a risk assessment process to ensure that any potential risks or threats are identified so that some safeguards can be put in place. Black hat thinking means that teams can anticipate and prepate for possible challenges.

Reverse brainstorming

My favourite way to facilitate Black Hat Thinking is with a reverse brainstorm. This means we look at how to do the opposite of whatever goal it is that we are striving to achieve. “How can we ensure that all of our managers fail?” or “How can we make sure that students never learn to read?” or “How can I become the worst leader of all time?” are some fun examples to play with. In one particularly memorable example in a secondary school context we reverse brainstormed “How can we make sure our students are unprepared for the future?” and there were some shocking truths suddenly up for discussion when some teachers recognised that their current pedagogy was setting kids up to fail…

The next step of a reverse brainstorm is to be as creative as possible and come up with ‘all the ways’ to make the opposite happen. Go wild. Have fun. Then review.

The final step of the reverse brainstorm is to flip the brainstorm and negate all of the negative suggestions and rephrase them as positives. If one answer for a ‘how can we become the worst leader’ brainstorm was ‘be late for all meetings’ then the solution is ‘be punctual’. The black hat thinking and using the reverse brainstorm as a facilitation method means that all of the negatives have been predicted and the opposite risk managing opportunity can be identified to inform meaningful strategic planning.

Foreseeing the rocks

If you hate the idea of a reverse brainstorm, another useful metaphor is a boat on its way to ‘some magical island outcome’. This method is great for visual thinkers. Draw a boat and draw a bunch of rocks and waves and kraken and pirates and whatever else you think might get in the way of a journey and then set about naming the hazards. What are all of the things in the way? Once this is done, you can start to strategise what your marine chart/road map might need in order to steer around those obstacles.

Both of these strategic planning experiences require the black hat to be donned - and like a black wizard’s hat, the outcomes of black hat thinking can be magical.

Black hat thinking is thinking about the weather, the rocks and the crew’s capabilities to better avoid disasters.

Making some magic

If you know where you don’t want to go, then you can forge a better path to your rightful destination. Similarly, knowing all of the bad things that ‘might’ happen, means that you can plan to avoid and manage risks accordingly.

Recently I have been working on an ‘AI in the Classroom’ rubric and black hat thinking has really helped me to consider best use case scenarios - only because I’ve spent time thinking of worst use case scenarios and then sought to fix them through reverse engineering.

The black hat brainstorm highlighted worst use of AI as ‘mindless prompting’ and ‘worksheet pumping’, ‘biased without balance’, ‘industrial model on steroids’, ‘data misusing’ and ‘environmentally disastrous’ (to name a few) which can then inform more critical analysis of how to mitigate these risks with educated, mindful, purposeful and policy-protected best uses to help me to design my self-assessment rubric. (This is coming soon).

So what kind of black hat will you wear?

Of course, you don’t need a real hat - but imagining your own black hat strategy to make better decisions in your workplace or classroom is a great thinking strategy to adopt if you want to know better to do better.

All These Engineers and No Sheddery

Sheddery isn't about physical spaces; it's about fostering mental and collaborative environments where ideas flourish and fast failure is a stepping stone to success. From napkin sketches to world-changing ideas, 'sheddery' promotes a shed load of continuous improvement, innovation and collaboration. Let's unpack what it means in this blog.

AI and Policy: A FLOW Guide

If you want to keep up with AI, you can’t. At least that’s what it feels like. That is the truth I’ve been struggling to come to grips with as I navigate the ethics of AI use in classrooms and workplaces and observe the exponential growth of AI tools for teachers and workers. There are new developments every second. And then there are considerations so far beyond the basics of intellectual property and data sharing… so let’s get stuck in. Read on to learn about some key considerations for policy writing as well as A FLOW model for testing and reflecting once it is written.

I recently presented a workshop on AI tools in the classroom grounded with a whakatauki as well as a metaphor. I used the ocean as a metaphor because, even if we feel well-informed at the moment, it is more than likely that we’re just paddling in the breakers because the flow on effects of our use of AI is vast and deep - like an ocean. It is a wicked problem.

My choice of whakatauki is also fitting - Kia mate ururoa, kei mate wheke - to fight like a shark rather than give in like an octopus. My challenge to all users of AI is to consider the why before diving right on in. Resist the status quo and resist doing something just because it is easier. Consider the repercussions and thrash about before you make a decision. Thrash to push for better learning experiences. Thrash for better pedagogy and androgogy. Thrash to strive for a better learning experience for all learners - and don’t accept the models that are just because they are. Just because it might be fast, doesn’t mean it is good.

So many AI tools packaged for educational purposes sadly perpetuate 20th Century knowledge models and do not ‘push the envelope’. My hope is that, with more knowledge of how AI models are programmed and more knowledge of biases and preferences as well as the ‘why of AI’, that those who work in education and training will work harder to make AI do something more responsible. ethical and transformative. Potentially writing useful policy is the first step in designing clear guidelines.

What should you include in an AI policy?

AI is evolving so rapidly that it is important for schools and workplaces to ride the wave. We need to be informed about which AI tools are fit for purpose and need to be able to approve specific tools, outcomes and systems. For this, we need to have clear objectives about when the use of AI is appropriate and be able to give recommendations for how and why information can be appropriately shared.

At this stage, it is useful to consider some user stories of when and how AI might be used and also to explore scenarios with a ‘black hat’ in order to consider the long term implications.

Set boundaries and design systems:

What boundaries need to be put in place about data sharing? (No names, no confidential information, no data shared without permission)

What are the expectations of the institution or organisation? (AI can be used for ideation assistance or proof-reading but not for any published content)

Who are the people who might oversee governance of AI? (And how might it be monitored?)

Consider the legal implications:

What disclosure regulations need to be in place for data sharing?

How can you safeguard against discrimination and bias?

What are the internal processes for data breaches?

Who is liable for data breaches or authenticity queries?

What industry-specific regulations can you draw on?

What are the terms of the AI tool/s that is/are being used?

How is intellectual property safe-guarded?

What are your internal systems for governance and monitoring?

What happens if there is a breach?

Managing Risk:

What permissions and guidelines need to be established to indicate which tools are approved and how they are used?

What risk assessments need to be undertaken against specific scenarios?

What are the regulations against outputs? Must they be reviewed by humans before publication?

How will issues or incidents be documented and reviewed?

Ethics:

AI does not come without an environmental footprint - how might you mitigate your use of AI against your Sustainable Development Goals?

How can you monitor your increased environmental impact?

How might you offset your carbon footprint?

How might you make users more aware of both technical and environmental considerations?

How might you ensure the privacy of information shared?

Training:

A lot of organisations are presenting AI tools as if they are a quick win without unpacking legal, ethical and environmental considerations. How might you ensure that staff are informed and able to make informed decisions about whether their use of AI is appropriate, necessary or effective?

A FLOW model:

The following acronym could be useful for beginning your deep dive into AI.

A - Anticipate - explore some user stories or scenarios to predict different uses of AI without guidance. Use these scenarios to design policy and training to meet the needs of your participants.

F - Facilitate - facilitate a training session on the responsible and ethical uses of AI and design a workplace scenario for a test/launch activity.

L - Launch - provide a tool and some prompts and a desired output that relates to your context for employees to explore.

O - Observe - look for anomalies in the uses of AI and adapt policy accordingly. Allow questioning and exploration alongside staff as they navigate ‘the deep’.

W - Weigh - consider how effective parameters and policy are and consider the success of input vs output in terms of how the tools were used and what the outcomes were.

Repeat with a new cycle to iteratively design a flexing policy that is capable of riding the AI wave.

So what do you think? Writing AI policy and using AI is about so much more than time saving. We need to consider legal implication of data sharing and collection, governance and guidelines about purpose vs product, environmental implications, intellectual property considerations, disclosure regulations, risk assessments, best practice evolutions and more.

Some additional thinking prompts:

How can we recognise and address bias in LLM training?

How can we push for more inclusive data sets for training?

What are our processes for consent?

What might our incident protocols be if the use of AI leads to negative outcomes?

Who should be involved in feedback loops about the use of AI?

How can AI be used to ensure equitable outcomes?

I recently read that creating an AI generated image uses the same amount of energy as charging a cell phone. This made me second-guess my use of AI as the carbon footprint aspect had not been front and centre for me - and knowing this has changed my usage as I want to be a more conscious consumer and my values align strongly with sustainability and the SDGs.

The key takeaway is that we need to be critical and aware of our use of AI so that we can actively contribute to a culture of proactive accountability and sustainability.

What do you think? Did I leave anything out?

References: Create your AI Policy, Clayden Law, e-book, 2024.

Best Practice and Burnout

I have recently returned to the classroom after working as a consultant and advisor for the last three years. Working in the private sector and working for the Ministry of Education more recently has offered me some fantastic opportunities to refine my understanding of what ‘best practice’ looks like. The ‘ideal state’ of optimal curriculum linking, deliberate use of data, systematic planning, agentic learning experiences, literacy/numeracy rich task design, critical thinking and 21st Century ‘make the pie’ pedagogy is my goal and this has been my active lens for advising teachers with ways to be better practitioners. Of course the ideal state is the goal - but there is a big but… let me explain.

Smilingly teaching on the outside, but on the inside, teachers are overloaded and suffering from incentivitis.

Being in the classroom is a humbling reality check. No change is a quick change and there are no ‘quick fixes’ for embedded teacher, learner (and leader) habits.

On the ground and knowing the ideal state lends itself to a bit of a panicked scramble. Where are the systems? What is the pedagogy? Where is the agency? Why is this not aligned to the curriculum? Where is the literacy? Where is the digital fluency? Where is the culture of collaboration? (So many questions!) When you know what best practice looks like (in the classroom and beyond the classroom, in management systems, in school-wide systems, in leadership styles etc), it can feel like a veritable swamp. We come home exhausted trying to fix so many things. So this is where a reality check needs to come into play.

Because best practice can be a perfectionist pedagogist’s undoing.

For me (on a personal level), I can see so many things that need to change - yet I need to remember that all change needs time and consistency to be effective. Also, I am one human. Further, I am one human who also has a family life and a ‘parent hat’ to put on as well as ‘partner hat’ and a ‘friend hat’ etc. To dedicate ALL of my time to the pursuit of excellence in all areas is commendable - but realistically not possible. I have to admit my human fallibility.

The first step is to take stock of the things we can change. We can add some systems to our classrooms. We can schedule student interviews. We can target specific data with our planning. We can reflect on our lesson sequences and look for ways to tweak them to be better for next time. We can give our students more opportunities to be critical thinkers and agentic learners. We can give them more opportunities to create with technology. Most importantly, We CAN strive for best practice - but we need to do it incrementally.

One thing that I have found particularly useful is using padlet as a kanban for next steps. Breaking down my big picture ideas into smaller chunks is a sanity saver. I still have my ideal state in mind and I can add small tasks to my kanban that will allow me to make incremental steps towards the end goal. (Check out my previous blog post about ‘The Kaizen Classroom’).

An example: For scaffolding the skills the students need for making good learning decisions daily is: Create a visual map of the lesson, print as a poster, get button magnets for showing where we are up to, design reflection activities for decision-making, create opportunities for decision-making, design survey for student voice, track engagement using Schoolytics (a handy plugin you can use with Google Classroom), trial, reflect, tweak.

An example of my ‘making good decision’ classroom road map. It is a useful way to chunk a lesson into clear sections and students have a ‘pick a path’ opportunity to explore different activities to anchor or apply their learning.

The burnout phenomenon among teachers is very real indeed. Teachers have ‘incentivitis’ and are constantly shifting and adapting to meet the requirements of new incentives. PLD funding is limited, effective PLD is hard to find (I can help with this) and time to implement actionable steps post-PLD is rarer still. It is no wonder that New Zealand’s education system is in a state of crisis.

So what is the solution?

Best practice needn’t be a pie in the sky that is unachievable. The truth is that if you are making small steps towards ‘better’ practice then this is something that should be noticed and rewarded. The solution is that we need to maintain a best practice vision and keep stepping towards it.

Keep a diary of ‘small steps’ that you can take in order to inch closer to the best practice model that you have in mind.
Determine what best practice actually looks like for you - what is most important?
Notice what key actions you have tried and keep a record of the steps
Use a Kanban to track your progress (‘Doing’ cards can be shifted to the ‘Done’ pile)
Share the load with others - collaboration is a great way to reduce your workload
Share failures as well as successes - what not to do is sometimes just as useful to know as what to do OR you might be able to troubleshoot better strategies together
Don’t give up on what best practice can be
Connect with others with a similarly optimistic vision for education (There are so many naysayers and fixed mindset people who cloud the vision for change. Avoid them.)

If you know what best practice looks like and then look around and feel like it ‘too far a star’ then look for a ‘near star’ marker to head to first. If you feel overwhelmed, swamped and depressed about the status quo, you need to remember that striving for better is possible - but also that it will take time.

Assess where you are at. Make a mark. Point to your far star and start marching there one step at a time. Don’t give up.

If you have a ‘far star’ in mind as a teacher or as a leader, let’s connect to formulate an achievable action plan together.

The Teaching Tree - Dive into the Archive

I have been happily blogging on my Wordpress ‘Teaching Tree’ Blog for years and, even though Squarespace says that I can import all of my content across to this website, I am yet to figure it out.

In the interim, this is a ‘satisficing’ measure - this blog can be window to all of the blogging and writing I have been doing over on my theteachingtree.blog

On my blog, you can find teaching tips and tricks, reflections on what works or why we need to try harder to make things work, general musings on pedagogy, poetry, an education manifesto and more.

This content is in the magic portal on its way over to this website but the time-space-time-life-reality continuum may cause some delays.