Tag: innovation

Imagination, Augmented: How AI Can Be a Creativity Amplifier

Artificial Intelligence, CreativityPublished onUpdated on

Your Snapshot

A summary of the key insights from this issue

⬩ Research shows Large Language Models (LLMs) like ChatGPT-4 generate diverse ideas faster and with higher quality than humans, benefiting creative problem-solving.

⬩ AI can exhibit types of creativity—combinational, exploratory, transformational—sometimes exceeding human creativity by exploring broader possibilities and taking more risks.

⬩ The future of creativity combines AI and human input. AI enhances creativity, but requires human oversight for idea evaluation. Real-world applications like TextFX, an AI-aided tool for rap writing, exemplify how AI can empower human creativity.

Can artificial intelligence (AI) unearth hidden layers of creativity within us? Have you ever wondered if a machine could help you generate ideas more swiftly, diversely, and effectively?

A burst of innovation often emerges when our minds are sparked by an external stimulus. AI, particularly Large Language Models (LLMs) like ChatGPT, can serve as that catalyst, enhancing our ability to generate ideas and solve problems creatively. This is because AI not only processes information at high speed but also explores a vast universe of possibilities beyond our immediate perception.

Harnessing the power of AI requires some effort, but it can significantly amplify our creative prowess and transform the way we innovate. The future of creativity is not just human or AI, but a blend of both, offering us a unique opportunity to reach new heights of creative potential.

The Power of LLMs for Idea Generation

How do Large Language Models (LLMs) like ChatGPT stack up against humans in generating ideas?

​A recent study by researchers from Cornell Tech and the Wharton School tackled this question. They tasked ChatGPT-4 and students from an elite university with generating new product ideas for the college student market, retailing for less than $50.

While it’s no surprise that LLMs like ChatGPT can swiftly produce ideas, the study unearthed some unexpected results regarding AI’s quality.

ChatGPT-4 not only churned out ideas faster than students, but the ideas were also, on average, deemed higher in quality based on purchase-intent surveys. Moreover, they displayed a higher variance in quality. This variance indicates that ChatGPT-4 can produce a broader range of ideas, some excellent and others not as good. This diversity benefits creative problem-solving, raising the likelihood of finding standout solutions.

Interestingly, the paper highlighted that feeding ChatGPT-4, a few highly-rated ideas, further improved its performance. It concluded that, for focused idea generation, a human using ChatGPT-4 is roughly 40 times more productive than a human working alone.

Can AI Be as Creative as Humans?

The debate around whether AI can match human creativity or even surpass it continues. Before we dive into this question, let’s first define creativity.

Margaret Boden, a renowned philosopher and cognitive scientist, defines creativity as the ability to produce something new, valuable, and surprising. She identifies three types of creativity:

  1. Combinational (combining existing ideas in new ways),
  2. Exploratory (exploring the possibilities within a given framework),
  3. Transformational (transforming the framework itself).

According to her, AI can exhibit all three types of creativity, albeit within certain constraints. While she acknowledges that human creativity has nuances that AI has yet to replicate, she believes that human creativity isn’t unique or superior to AI creativity.

Echoing this sentiment, mathematician and author Marcus du Sautoy suggests that AI can exceed human creativity in certain areas. He attributes this to AI’s ability to explore more extensive possibilities, maintain objectivity, and take more risks.

Implications for Innovation

The study by Cornell Tech and the Wharton School suggests that LLMs like ChatGPT can significantly enhance your creative prowess, generating many novel concepts. However, with idea generation becoming quick, cheap, and easy, effective processes for evaluating and filtering the most promising ideas become crucial.

​MIT professor Mitchel Resnick views AI systems as a new category of educational resource with unique strengths and limitations. He highlights that while LLMs can inspire human creativity by providing new examples to build upon, they should not replace human ideation.

“encourage learners to use ChatGPT and other generative AI tools not to produce the final result but as a resource throughout their own creative process.”

Mitchel Resnick

How AI Can Collaborate with Humans to Create Multiplicity

Professor of Engineering and artist Ken Goldberg presents a new paradigm of “multiplicity,” where humans and machines collaborate to generate diverse ideas. He argues that multiplicity, defined as “the quality or state of being multiple or various,” is indispensable for creativity.

Goldberg provides examples of multiplicity in practice, such as crowdsourcing platforms like Amazon Mechanical Turk or Wikipedia and collective intelligence systems like Google Search or Netflix Recommendations.

He suggests that diversity can help overcome human and AI creativity challenges, including cognitive biases, information overload, groupthink, or ethical dilemmas.

Even the simple prompt, “What might I be missing?” can shift your perspective and challenge a bias.

AI and the Future of Creativity: A Case Study of TextFX

In practical terms, we can expect to see the convergence of generative AI capabilities into tools specifically designed to bolster elements of the creative process.

A prime example is the collaboration between rapper and professor Lupe Fiasco with Google and the Palm API. They developed a suite of AI-powered tools called TextFX, created to aid in the rap writing process.

​TextFX doesn’t generate complete lyrics. Instead, it “explodes” words into multiple phonetic possibilities and explores a vast universe of potential meanings and interpretations. This design is inspired by Fiasco’s technique of dissecting words and phrases into various semantic and phonetic components.

The suite includes ten tools to generate similes, create acronyms, and parse words. It can take any word as input and provide many interpretations, similar to Fiasco’s approach in his songwriting. I can see lots of uses to support young people and their writing or creative thinking.

However, the aim of TextFX isn’t to replace the rapper in the songwriting process. Instead, it’s designed to empower and inform them. Fiasco emphasises that the joy of rapping stems from work and struggle, and these AI tools facilitate that journey.

The promise of AI in the creative landscape is vast. Not as a replacement for human creativity but as a collaborator. AI amplifies our creative process and pushes the boundaries of what’s possible. Do you want to be 40 times more productive?

The future of creativity is not just human or AI but a blend of both, enriching our capacity to innovate and solve problems.

⏭🎯 Your Next Steps

Commit to action and turn words into works

⬩ Explore and Experiment: Start using AI tools like ChatGPT in your creative process, learning their capabilities and your limits through hands-on experimentation.

⬩ Learn and Develop: Invest in AI-related training to understand its potential and ethical implications in your field.

⬩ Collaborate and Share: Share your AI experiences with your team, fostering a collaborative environment for navigating the future of creativity with AI.

🗣💬 Your Talking Points

​Lead a team dialogue with these provocations

⬩ What are the potential benefits and challenges we might encounter as we integrate AI into our creative work?

⬩ What might the future look like as we continue to blend human creativity with AI’s capabilities, and how can we prepare ourselves for this future?

⬩ As AI becomes more integrated into our creative processes, what ethical considerations should we keep in mind?

🕳🐇 Down the Rabbit Hole

Still curious? Explore some further readings from my archive

⟶ Time for Creativity in Schools – Tom Barrett (edte.ch) My article explores the challenges and possibilities of fostering creativity in schools, which often operate on rigid timetables and structures that hinder the creative process.

⟶ Can machines be more creative than humans? | Arthur Miller | The Guardian The article explores the implications of AI art for human creativity and knowledge. It suggests that machines may be able to surpass human creativity by making connections across different fields and domains. It also proposes that machines may enhance human creativity and help solve global problems.

⟶ A philosopher argues that an AI can’t be an artist | MIT Technology Review The author argues that creativity is not just novelty or skill, but a vision of the world that changes our understanding of what is good, true, or beautiful. Machines can mimic or assist human creativity, but they cannot produce it on their own.

⚖️ How to Lead Change with the ADKAR Model (and Why It Works)

Innovation, LeadershipPublished onUpdated on

Dialogic #322

Leadership, learning, innovation

Your Snapshot

​ A summary of the key insights from this issue

⬩ The ADKAR model of change consists of five elements: Awareness, Desire, Knowledge, Ability, and Reinforcement.

⬩ The model of change shows you how to move from awareness to intention, and from intention to implementation

⬩ The real challenge of leading change and transformation is how to balance between planning and adapting, between order and chaos.

explosion of creative ideas, duotone --ar 3:2 --v 5.1 --s 250

In today’s issue, we explore a model of change which begins with changing awareness.

I’ve had the opportunity to collaborate with several school teams over the past few weeks, discussing various areas of professional learning, including fostering a culture of feedback, creating conditions for innovation, and building a more effective cognitive toolkit.

Throughout these sessions, I’ve reflected on the transition from “I am aware…” [Awareness] to “I am going to change this…” [Intention], and the potential for encountering a stall in our growth efforts.

Hence, I was eager to explore models and frameworks of change that emphasise transforming awareness. Below, I have outlined the ADKAR model from Jeffrey Hiatt, a British author and change management expert.

This model encompasses five stages: Awareness, Desire, Knowledge, Ability, and Reinforcement. Incorporating the ADKAR model can provide a structured approach when leading new projects or addressing the question of “how do I get buy-in?”—though not my preferred phrase, it is a concept that often arises in educational change.

Additionally, the ADKAR model can be instrumental in personal growth and individual change endeavours, helping to overcome potential stalling points along the way.

Awareness

The first step is to create awareness of the need for change. This means communicating the reasons, goals, and benefits of the change to colleagues in a clear and compelling way. It also means addressing any questions, concerns, or objections that they may have. The aim is to help people understand why the change is necessary and desirable. What might be obvious to us, could be unknown to others – don’t make assumptions.

This step addresses the challenge of lack of understanding about the need for change. If individuals don’t understand why change is necessary, they may resist it or be apathetic.

Some strategies to create awareness are:

  • Use multiple channels and formats to communicate the need for change
  • Provide data and evidence to support the change (e.g., research findings, best practices, success stories)
  • Take your time and create spaces for feedback and dialogue

Desire

The second step is to generate a desire to support and participate in the change. This means motivating employees to embrace the change and commit to it. It also means addressing any barriers or risks that may prevent them from doing so. The aim is to help employees feel the change is in their best interest and aligned with their values.

Your focus on Desire tackles the issue of resistance to change. Even if individuals understand the need for change, they may not want to change. This could be due to fear of the unknown, comfort with the status quo, or disagreement with the change.

Some strategies to generate desire are:

  • Highlight the intended impact, positive outcomes and benefits of the change on personal or professional growth
  • Address the negative consequences and costs of not changing. The missed opportunities, student disadvantage, diminishing wellbeing or cultural stagnation.
  • Involve employees in the change process and empower them to build a rationale, co-design opportunities, pilot innovations, and share feedback.

Knowledge

The third step is to provide knowledge of how to change. This means educating employees on the skills, behaviours, and actions required for the change. It also means providing the necessary resources and support to learn and apply them. The aim is to help employees acquire the competence and confidence to change.

Address the challenge of not knowing how to change. Individuals may understand the need for change and desire to change, but if they don’t know what to do differently, they can’t change.

Some strategies to provide knowledge are:

  • Offer training and coaching programs that are relevant, timely, and accessible.
  • Provide tools and materials that are practical, user-friendly, and flexible.
  • Create learning communities that are supportive, collaborative, and diverse (e.g., peer groups, mentors, experts, etc.)

Ability

The fourth step in the ADKAR change framework is the ability to implement the change. This means facilitating employees to practice and perform the new skills, behaviours, and actions required for the change. It also means monitoring and evaluating their progress and performance.

This step tackles the inability to implement the change. Even if individuals know what to do differently, they may not have the skills, resources, or support. Our focus here is on capacity and capability building.

Some strategies to enable ability are:

  • Provide safe opportunities to apply and experiment with new skills, behaviours, and actions. Test and tinker together.
  • Offer feedback and celebrate the efforts and achievements teams have made.
  • Provide coaching and ongoing support in building capacity. Respond quickly to difficulties and challenges.

Reinforcement

The fifth step is to ensure reinforcement to sustain the change. This means reinforcing the new skills, behaviours, and actions. It also means celebrating and sharing their successes and learnings.

This final step addresses the challenge of change not sticking. Even if individuals have successfully implemented the change, they may revert to old behaviours if the change is not reinforced.

Some strategies to ensure reinforcement are:

  • Create stability and certainty in the messaging by maintaining focus on core areas of change. Don’t jump to something else!
  • Offer regular reminders and cues for consistent adoption and integration. Share and highlight stories of change and success.
  • Create opportunities and resources for ongoing learning and development. Invest in reminders, refreshers and retraining.

⏭🎯 Your Next Steps

Commit to action and turn words into works

⬩ Use the ADKAR model to reflect on the current level of awareness, desire, knowledge, ability, and reinforcement of your change experiences.

⬩ Analyse the results and identify the gaps and barriers that might prevent change and innovation.

⬩ Tap into the history of change in your organisation. What works? What’s missing?

🗣💬 Your Talking Points

Lead a team dialogue with these provocations

⬩ Why do we need to change and innovate? What are the goals and benefits of doing so?

⬩ How do we feel about the change and innovation? What are the key motivations for change?

⬩ What does co-design look like for us?

🕳🐇 Down the Rabbit Hole

Still curious? Explore some further readings from my archive

⟶ 3 Mental Models From Economics For Educators To Enhance Your Innovation – Tom Barrett (edte.ch) My article introduces three mental models from economics that can enhance innovation in education: the network effect, the sunk cost fallacy, and compounding.

⟶ Adapting education innovations and their ‘knock-on effects’ in the time of COVID | Brookings​
The article from the Brookings Institute argues that adaptations not only address the direct challenges but also trigger other changes and effects that alter the larger system. Some of these effects are positive, some are negative, and all will continue to evolve.

⟶ The ADKAR Model: Why it works​
This article highlights the benefits of using the ADKAR model, such as being easy to learn, outcome-oriented and applicable to any type of change. The article emphasises that change happens one person at a time and that the ADKAR Model is a powerful tool for supporting individuals through change.