Next Path’s AI Pathfinder Competencies
When I decided to direct Next Path Design toward AI this summer, I began by reviewing existing literature to determine the necessary skills and knowledge for effective AI use. After years of working in adult education and instructional design, I’ve learned the importance of having a solid set of competencies as the foundation for the skills and knowledge people are acquiring.
I’ve been digging into the academic literature and other publications focused on AI literacy and fluency. With a main focus on the needs and applications of AI for adults, I’m actively reviewing peer-reviewed studies, AI literacy frameworks, and reports for all ages from a range of organizations. Here are several of the dozens of sources that have influenced my analysis:
- Meta AI Literacy Scale (MAILS)
- World Economic Forum: Future of Jobs Report
- OECD: AILit Framework
- UNESCO:
- Stanford’s AI Index Report
- McKinsey: Superagency in the workplace: Empowering people to unlock AI’s full potential
- Digital Promise AI Literacy Framework
- EDUCAUSE AI Literacy in Teaching and Learning (ALTL)
Human-In-The-Loop
My focus centered on AI integration and the knowledge and skills needed to work effectively with AI. While there is a lot of hype surrounding the threat of job replacement, a common thread among the sources is a focus on how AI is fundamentally changing the way work gets done.
Many emphasize the importance of supporting a human-in-the-loop approach, where AI augments human abilities rather than replacing them entirely. This requires effective human input, oversight, and collaboration with AI and depends on a person’s ability to understand, evaluate, and strategically interact with AI systems.
Yet, most of the AI education I came across was not focused on these skills, but was more often limited to tutorials on tools or tips on prompting. The gap between the required skills and knowledge to select, use, and evaluate AI and the available training led me to the what and why of Next Path Design’s upcoming offerings. More on that below.
Building a Set of AI Competencies
Ten Competency Categories
From this analysis, I worked to develop what became the AI Pathfinder Competencies, a set of 10 competency categories that include:
- AI Confidence and Resilience: Building readiness and adaptive capacity to contemplate the opportunities, threats, and uncertainty associated with AI integration.
- AI Tool Fluency and Technical Skills: Developing practical knowledge for selecting, configuring, and integrating AI tools effectively.
- AI Output Evaluation and Critical Thinking: Using critical analysis skills for evaluating and validating AI-generated information.
- Human-Centered AI Collaboration and Communication: Working productively with AI while preserving human agency, judgment, and well-being.
- AI-Powered Innovation and Creative Problem Solving: Leveraging AI to amplify human creativity and innovation capacity.
- Data Literacy and Information Management: Analyzing how data quality affects AI performance, privacy, and decision-making reliability.
- Ethics, Risk and Compliance: Navigating ethical principles, risk assessment, and compliance requirements for responsible AI use.
- Economic Impact and Business Strategy: Measuring AI’s economic implications for personal productivity, careers, and organizational strategy.
- AI Change Readiness and Team Enablement: Managing change during AI adoption processes.
- Future Readiness and Strategic Foresight: Anticipating AI trends and building adaptive capacity for future technological developments.
This progression within the competency categories is intended to mirror how AI adoption typically happens in workplace environments. For example, individual experimentation leads to practical integration, which enables team collaboration and eventually supports more holistic organizational transformation.
PATH Complexity Levels
I also incorporated four layers of complexity within the AI Pathfinder Competencies that reflect a hierarchy of competency development, including:
- P = Proficiency – Beginner: Foundational awareness and basic skills
- A = Application – Intermediate: Practical implementation and integration
- T = Transformation – Advanced: Team leadership and organizational change
- H = Holistic Integration – Expert: Strategic governance and culture
From this structure, I drafted 200 specific learning aims based on the 10 competency domains, 4 PATH levels per domain, and 5 learning aims per level. 10 x 4 x 5 = 200!
My Human-AI Collaboration
Throughout this process, I collaborated with my AI Assistants to help me discover sources, analyze them, and review my work on the completed competencies. In addition to AI-based search tools like Consensus and Semantic Scholar, I utilized various AI Assistants, including Gemini, Claude, ChatGPT, Perplexity, and Copilot, to aid in information discovery and analysis.
What’s Next?
The AI Pathfinder Competencies now guide my work at Next Path Design. As a first draft, I believe it effectively covers a relevant set of AI capabilities identified in the literature, which go well beyond just learning tool features. Our focus on ethical reasoning, strategic thinking, communication skills, and adapting to change sets Next Path Design courses apart from other collections of tool tutorials.
My upcoming AI Pathfinder Foundations course primarily covers competency categories 1-3, providing a foundational knowledge for individuals. Future courses will move through additional competencies within the application and transformation levels, ultimately reaching the strategic integration competencies.
More to come as I continue showing my work on this journey!
– Jennifer
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Good Morning Dr. Maddrell,
As your student last year, I genuinely appreciated your embarkation on familiarizing yourself with AI. It is a strength to find innovation and boldness in higher education. Looking at this work, you elude to the audience of this course, but I did not want to make assumptions. I am curious if you could provide a breakdown of who your intended audience is in order to hone the approach to each of your competencies. What research have you done specifically about this audience and their needs in each of these competency areas? Have you ordered the competencies in any specific way? Finally, I think PATH is brilliant for instructional designers as a way of breaking down the characteristics of each group-easy and straight-forward. I was wondering what parameters defines each group more specifically. I am curious to see the structure! You are so thorough with your feedback, and I am hoping you get an equal amount from others. Thank you for sharing!
Hi, Heather. Good to hear from you. My intended audience for Next Path program is adults with an original broad focus on AI in the workplace. However, as I began to drill down into relevant skills and knowledge suggested in my literature search and dug into what topics people were seeking versus what was being offered (e.g., from analysis of YouTube, free AI courses, etc.), I decided to focus my initial Foundations course on learning aims that more closely align with the approaches that are typical in basic digital or information literacy and technology integration courses.
In terms of breaking down my intended audience characteristics, I’m leaning into Rogers’ Diffusion of Innovations to speculate that digital innovators and early adopters aren’t the target market for the Foundations course. I have other ideas aligned with these folks in the pipeline.
As an aside, my hunch is that it’s these innovators and early adopters who are creating existing courses that have skipped over the vast majority, who have barely dipped a toe into AI. This means my aim is to meet the rest of the Rogers adoption curve where they are with an emphasis on these areas:
– AI Confidence and Resilience: Building readiness and adaptive capacity to contemplate the opportunities, threats, and uncertainty associated with AI integration.
– AI Tool Fluency and Technical Skills: Developing practical knowledge for selecting, configuring, and integrating AI tools effectively.
– AI Output Evaluation and Critical Thinking: Using critical analysis skills for evaluating and validating AI-generated information.
– Human-Centered AI Collaboration and Communication: Working productively with AI while preserving human agency, judgment, and well-being.
– Data Literacy and Information Management: Analyzing how data quality affects AI performance, privacy, and decision-making reliability.
– Ethics, Risk and Compliance: Navigating ethical principles, risk assessment, and compliance requirements for responsible AI use.
Again, good to hear from you.
Jennifer