This is a fascinating article. And I certainly see, and embrace, your points regarding your Zone 2 and Zone 3. I'm having a little trouble with grasping your point about Zone 1. My field is the law. Let's consider a first-year law student in her research & writing (legal methods) course. It's not unusual for the first legal memorandum that is assigned to include a small group of cases, selected by the instructor, since the class has not yet gained much experience in researching relevant cases for themselves. I'm trying to envision where AI would fit into this assignment/assessment. I don't think I would want the students to assign the "substantive" work of reading, digesting, and summarizing the individual cases to AI. I'm hard pressed to know just what I would want AI to be asked to do. Perhaps this traditional assignment has lost its efficacy, since one can hardly prevent the students from assigning these "substantive" tasks to AI. Perhaps your response is that in Zone 3, the students would be expected to evaluate AI's review and summation of the cases and document that process. If I got that right, I wonder if this exercise would be cognitively superior to the students performing the "substantive" work themselves. At first blush, I might prefer a Zone 1 approach. And since I can't prevent the students from assigning the analytical tasks ---e.g., reading and briefing the assigned cases---to AI, I might require them to perform that exercise in class rather than outside of class. Am I on point here, or have I totally misunderstood you?
Thank you for this thoughtful response, and for the concrete legal methods example – it actually illustrates the tension at the heart of the article quite well.
I should clarify one thing: what I labeled as Zone 1 was not a recommendation. It was a description of where most educators instinctively land – constrained, restricted AI use – and an argument that staying there is neither sustainable nor educationally sufficient. Your instinct to move the assignment into class (blue books) to prevent AI use is a common response, and I understand the appeal. But it addresses the symptom rather than the underlying question, which is what the learning goal actually is.
Your legal methods example is worth considering. If the goal is for students to practice reading and briefing cases, then yes, assigning that to AI defeats the purpose. But the more interesting question is whether the goal itself should evolve. What if students briefed the cases themselves AND then interrogated what AI produced – identifying where it flattened nuance, missed procedural posture, or got the holding subtly wrong? That requires a deeper command of the skill, not a shallower one.
That is closer to what I see as the transformation space, drawing on the Wang and Zhang framework. It is not about evaluating AI instead of doing the work. It is about doing the work and then using AI to push your thinking further – which, in legal education, has real professional relevance given how quickly AI tools are entering legal practice.
You have not misunderstood me entirely – you are asking exactly the right questions. The next challenge is to decide whether we want the deeper learning or if we want to push to keep the same constraints we have had in the past.
I'm not sure that I concede "the same restraints we have had in the past." I suppose I fancy that the legal research and writing course (Legal Methods) enabled the students to learn precisely the skills they would practice as new associate attorneys in the law firms and judicial chambers they would join after graduation. Having said that seems to beg the question of AI's impact on those entry level jobs. Attorneys were quick to use chatbots to draft briefs... too quick, as you probably have read. A few such "early adapters" :) suffered national embarrassment in the news media when the cases they cited proved to be chatbot hallucinations. I've queried senior attorneys whom I know about what will happen to the traditional apprenticeship in the legal profession, if and when (and I think 'when' is more likely than 'if') first-year associates are no longer needed to do research and writing. One thought I have is that law schools might place much greater emphasis on other skills: litigation, mediation, negotiation, conciliation...to name a few. Such skills training was available 45 years ago, when I attended law school, but it wasn't required, nor was it much emphasized by faculty advisors. Consequently, a student could get through three years of law school with almost no experience in those skill sets, if s/he so desired. The legal methods course could look quite different ala some of the "deeper command of the skill" to which you refer. I hope that's the direction we go, as GenAI chews its way up the professional-workforce food chain.
I appreciate these further thoughts and I agree that we need to build more skill building into our classroom experiences (experiential learning anyone?).
an interesting take—one thing I noticed when I went back to the paper is that the “zones” framing isn’t actually in Wang & Zhang’s analysis (it comes from a post-hoc interpretation of a curve).
Great points. My colleague Daryl Privott and I have found the LOVE framework (Logic, Originality, Verifiability, and Ethics) useful for helping faculty and students engage with AI more intentionally.
Your discussion of Zone 3 strategic delegation is especially helpful, because LOVE teaches students a structure for deciding what to delegate, what to keep as human work, and how to evaluate AI output critically. In that sense, it can support the shift from scattered AI use toward the purposeful, reflective engagement you describe.
Your point about the Blind Spot is equally important. We have found Verifiability especially useful for making AI use more visible, discussable, and tied to learning goals, which can shift faculty from trying to police AI toward designing assessments that support deeper reasoning.
I’d be eager to see more on assignment design, disclosure, and policy. Pedagogical problems deserve pedagogical solutions.
Thank you for the feedback and sharing what you do! Are you looking for disclosure and policy as it relates to assignment design? Or are you thinking of these as three separate topics?
That’s a great question. I see them as deeply interconnected. Effective assignment design essentially provides the structure where disclosure becomes a meaningful part of the process rather than just a boilerplate statement.
When an assignment is designed to require a 'Logic' or 'Verifiability' check, policy shifts from a set of restrictive rules into a collaborative agreement on how to use the tool ethically. I'd be especially interested in your thoughts on how to move disclosure beyond a simple syllabus statement and into the actual metadata or reflective components of a student's work.
This is spot on. I always recommend that we have real conversations on an ongoing basis with students. Model the behavior we want to see. Show them how you are using AI. Show them when it is right and when it is wrong. If they ask you a question, show them how you think through getting answer. Blow their minds with how AI can empower your thinking. It should normalize as just part of how we work and think.
Zone 3 is the right destination and you've named the pedagogical path to it precisely.
The deeper structural question underneath it: Zone 3 learning produces a fundamentally different kind of human capability than the credential system was designed to measure or reward. A student who has spent three years developing genuine judgment, questioning assumptions, and directing AI toward original synthesis has built something real and valuable. But it doesn't show up on a transcript the way mastery of syntax does.
The assessment redesign you're recommending — oral exams, portfolios, AI transcripts — is the right direction within existing institutional frameworks. The more fundamental shift is building the attribution infrastructure that makes the Zone 3 learner's body of work directly legible and verifiable outside the institution entirely.
When the portfolio credential becomes a verifiable record of what a productive entity has actually produced — not curated by the student, not evaluated by the faculty, but structurally encoded at the point of production — the Johari Window blind spot closes by architecture rather than by awareness.
The equity point is the most urgent one. Zone 3 as a privilege of the resourced is the productive entity two-tier economy playing out in the classroom before the legal infrastructure even exists. Universal baseline access to capable AI agents isn't just an economic equity question. It's an educational equity question with the same urgency.
It is about Human - AI collaboration (not unlike other intelligent tools that have emerged in the past).
Lots of discussions around "Will AI take your job, the better question is "Will someone who knows how to collaborate with AI take your job?".
There is a concept used in financial analysis, namely “The Efficient Frontier” that gives us a perspective in the implementation of AI.
The Efficient Frontier:
It’s the set of “best” portfolios where:
• You can’t get higher returns without taking more risk, and
• You can’t reduce risk without lowering returns
Think of it as the “sweet spot curve” of optimal choices.
Using this concept of “The Efficient Frontier” to example how humans and AI should collaborate would lead us to the question:
Instead of organizations asking: How much AI should we use?
The better question is: What combination of human judgment and AI capability puts us on the efficient frontier of performance?
Human only and AI only are both suboptimal. The real advantage comes from operating on the efficient frontier of Human–AI collaboration and determining within a specific implementation where judgment and scale are combined, not traded off.
The "measuring the wrong zone" point is so important. Most of the research on AI and cognitive offloading measures whether students perform tasks with or without AI, which misses the question of whether they're developing the right thinking in the right moments. The zone of proximal development framing would actually reframe the whole debate. Offloading the stuff you've already mastered to focus cognitive effort on the edge of your competence isn't dangerous, it's just good learning.
Brilliantly clear and balanced, Tawnya. Thank you. It gave me lots to think about
Most educators are also in zone 2 with AI use.
This is a fascinating article. And I certainly see, and embrace, your points regarding your Zone 2 and Zone 3. I'm having a little trouble with grasping your point about Zone 1. My field is the law. Let's consider a first-year law student in her research & writing (legal methods) course. It's not unusual for the first legal memorandum that is assigned to include a small group of cases, selected by the instructor, since the class has not yet gained much experience in researching relevant cases for themselves. I'm trying to envision where AI would fit into this assignment/assessment. I don't think I would want the students to assign the "substantive" work of reading, digesting, and summarizing the individual cases to AI. I'm hard pressed to know just what I would want AI to be asked to do. Perhaps this traditional assignment has lost its efficacy, since one can hardly prevent the students from assigning these "substantive" tasks to AI. Perhaps your response is that in Zone 3, the students would be expected to evaluate AI's review and summation of the cases and document that process. If I got that right, I wonder if this exercise would be cognitively superior to the students performing the "substantive" work themselves. At first blush, I might prefer a Zone 1 approach. And since I can't prevent the students from assigning the analytical tasks ---e.g., reading and briefing the assigned cases---to AI, I might require them to perform that exercise in class rather than outside of class. Am I on point here, or have I totally misunderstood you?
Thank you for this thoughtful response, and for the concrete legal methods example – it actually illustrates the tension at the heart of the article quite well.
I should clarify one thing: what I labeled as Zone 1 was not a recommendation. It was a description of where most educators instinctively land – constrained, restricted AI use – and an argument that staying there is neither sustainable nor educationally sufficient. Your instinct to move the assignment into class (blue books) to prevent AI use is a common response, and I understand the appeal. But it addresses the symptom rather than the underlying question, which is what the learning goal actually is.
Your legal methods example is worth considering. If the goal is for students to practice reading and briefing cases, then yes, assigning that to AI defeats the purpose. But the more interesting question is whether the goal itself should evolve. What if students briefed the cases themselves AND then interrogated what AI produced – identifying where it flattened nuance, missed procedural posture, or got the holding subtly wrong? That requires a deeper command of the skill, not a shallower one.
That is closer to what I see as the transformation space, drawing on the Wang and Zhang framework. It is not about evaluating AI instead of doing the work. It is about doing the work and then using AI to push your thinking further – which, in legal education, has real professional relevance given how quickly AI tools are entering legal practice.
You have not misunderstood me entirely – you are asking exactly the right questions. The next challenge is to decide whether we want the deeper learning or if we want to push to keep the same constraints we have had in the past.
I'm not sure that I concede "the same restraints we have had in the past." I suppose I fancy that the legal research and writing course (Legal Methods) enabled the students to learn precisely the skills they would practice as new associate attorneys in the law firms and judicial chambers they would join after graduation. Having said that seems to beg the question of AI's impact on those entry level jobs. Attorneys were quick to use chatbots to draft briefs... too quick, as you probably have read. A few such "early adapters" :) suffered national embarrassment in the news media when the cases they cited proved to be chatbot hallucinations. I've queried senior attorneys whom I know about what will happen to the traditional apprenticeship in the legal profession, if and when (and I think 'when' is more likely than 'if') first-year associates are no longer needed to do research and writing. One thought I have is that law schools might place much greater emphasis on other skills: litigation, mediation, negotiation, conciliation...to name a few. Such skills training was available 45 years ago, when I attended law school, but it wasn't required, nor was it much emphasized by faculty advisors. Consequently, a student could get through three years of law school with almost no experience in those skill sets, if s/he so desired. The legal methods course could look quite different ala some of the "deeper command of the skill" to which you refer. I hope that's the direction we go, as GenAI chews its way up the professional-workforce food chain.
I appreciate these further thoughts and I agree that we need to build more skill building into our classroom experiences (experiential learning anyone?).
an interesting take—one thing I noticed when I went back to the paper is that the “zones” framing isn’t actually in Wang & Zhang’s analysis (it comes from a post-hoc interpretation of a curve).
I wrote a deeper breakdown here if helpful, especially on the difference between orientation vs. offloading: https://tinaaustin.substack.com/p/how-cognitive-offloading-ai-research?r=4a98uc&utm_campaign=post-expanded-share&utm_medium=web
Thank you Tina! This is very helpful and I appreciate the thorough discussion of the nuances. I will adjust my writing moving forward.
Great points. My colleague Daryl Privott and I have found the LOVE framework (Logic, Originality, Verifiability, and Ethics) useful for helping faculty and students engage with AI more intentionally.
Your discussion of Zone 3 strategic delegation is especially helpful, because LOVE teaches students a structure for deciding what to delegate, what to keep as human work, and how to evaluate AI output critically. In that sense, it can support the shift from scattered AI use toward the purposeful, reflective engagement you describe.
Your point about the Blind Spot is equally important. We have found Verifiability especially useful for making AI use more visible, discussable, and tied to learning goals, which can shift faculty from trying to police AI toward designing assessments that support deeper reasoning.
I’d be eager to see more on assignment design, disclosure, and policy. Pedagogical problems deserve pedagogical solutions.
Thank you for the feedback and sharing what you do! Are you looking for disclosure and policy as it relates to assignment design? Or are you thinking of these as three separate topics?
That’s a great question. I see them as deeply interconnected. Effective assignment design essentially provides the structure where disclosure becomes a meaningful part of the process rather than just a boilerplate statement.
When an assignment is designed to require a 'Logic' or 'Verifiability' check, policy shifts from a set of restrictive rules into a collaborative agreement on how to use the tool ethically. I'd be especially interested in your thoughts on how to move disclosure beyond a simple syllabus statement and into the actual metadata or reflective components of a student's work.
This is spot on. I always recommend that we have real conversations on an ongoing basis with students. Model the behavior we want to see. Show them how you are using AI. Show them when it is right and when it is wrong. If they ask you a question, show them how you think through getting answer. Blow their minds with how AI can empower your thinking. It should normalize as just part of how we work and think.
Zone 3 is the right destination and you've named the pedagogical path to it precisely.
The deeper structural question underneath it: Zone 3 learning produces a fundamentally different kind of human capability than the credential system was designed to measure or reward. A student who has spent three years developing genuine judgment, questioning assumptions, and directing AI toward original synthesis has built something real and valuable. But it doesn't show up on a transcript the way mastery of syntax does.
The assessment redesign you're recommending — oral exams, portfolios, AI transcripts — is the right direction within existing institutional frameworks. The more fundamental shift is building the attribution infrastructure that makes the Zone 3 learner's body of work directly legible and verifiable outside the institution entirely.
When the portfolio credential becomes a verifiable record of what a productive entity has actually produced — not curated by the student, not evaluated by the faculty, but structurally encoded at the point of production — the Johari Window blind spot closes by architecture rather than by awareness.
The equity point is the most urgent one. Zone 3 as a privilege of the resourced is the productive entity two-tier economy playing out in the classroom before the legal infrastructure even exists. Universal baseline access to capable AI agents isn't just an economic equity question. It's an educational equity question with the same urgency.
It is about Human - AI collaboration (not unlike other intelligent tools that have emerged in the past).
Lots of discussions around "Will AI take your job, the better question is "Will someone who knows how to collaborate with AI take your job?".
There is a concept used in financial analysis, namely “The Efficient Frontier” that gives us a perspective in the implementation of AI.
The Efficient Frontier:
It’s the set of “best” portfolios where:
• You can’t get higher returns without taking more risk, and
• You can’t reduce risk without lowering returns
Think of it as the “sweet spot curve” of optimal choices.
Using this concept of “The Efficient Frontier” to example how humans and AI should collaborate would lead us to the question:
Instead of organizations asking: How much AI should we use?
The better question is: What combination of human judgment and AI capability puts us on the efficient frontier of performance?
Human only and AI only are both suboptimal. The real advantage comes from operating on the efficient frontier of Human–AI collaboration and determining within a specific implementation where judgment and scale are combined, not traded off.
I love this! Thank you for extending the idea.
The "measuring the wrong zone" point is so important. Most of the research on AI and cognitive offloading measures whether students perform tasks with or without AI, which misses the question of whether they're developing the right thinking in the right moments. The zone of proximal development framing would actually reframe the whole debate. Offloading the stuff you've already mastered to focus cognitive effort on the edge of your competence isn't dangerous, it's just good learning.
Great research! Really helpful for those in the AI trenches of teaching, learning, and assessment. Thx much!
https://profclaytonwilliams.substack.com/p/religion-law-science-digital-and?utm_source=share&utm_medium=android&r=7mc63b