ARAD 2.0: AI-Responsive Assignment Design for Human Learning

Here is the question every faculty member needs to answer: Does your assignment still measure what it claims to measure when a student uses AI?

If the honest answer is no, you are not alone. A University of Reading blind study found that AI-generated exam submissions went undetected 94% of the time and often earned higher grades than real student work (Scarfe et al., 2024). That finding should stop every educator in their tracks. It means that across disciplines, polished output no longer proves learning. A beautifully written essay, a technically correct lab report, a professional-looking marketing plan—none of these reliably signal that a student understands anything at all.

ARAD 2.0: AI-Responsive Assignment Design for Human Learning is my answer to that problem. It is a goal-first, research-backed framework for designing assessments that use, limit, or restrict AI in ways that preserve learning, reveal thinking, strengthen judgment, and maintain fairness. It evolves the original ARAD framework I published in 2023 by replacing a binary AI model with a three-mode continuum, elevating process evidence and verification to core design principles, adding equity safeguards, and grounding every element in established learning science.

This is not a short read. ARAD 2.0 is a comprehensive framework, and I am going to walk through all of it: the theoretical foundation, the six core principles, the three assignment modes, the five-step GOALS design process, four fully worked redesign examples, an AI literacy primer, answers to the most common faculty objections, and a practical implementation guide. If you are a faculty member, instructional designer, department chair, or academic administrator trying to figure out how to design assignments that actually work in 2026 and beyond, this is the article to bookmark.

From ARAD to ARAD 2.0: Why the Framework Needed to Evolve

The original ARAD framework, published in 2023, introduced a five-step model captured by the mnemonic GOALS: Goal, Openness, Adapt, Link, Study. It gave faculty a practical way to move from panic to design, centering assignment decisions on learning objectives rather than AI policy. The original model made two core contributions. First, it insisted on goal-first design—AI decisions follow the learning objective, not the other way around. This aligns with Backward Design (Wiggins & McTighe, 2005) and with UNESCO and OECD guidance that AI use should be driven by pedagogical intent. Second, it introduced a two-path model distinguishing between AI-Integrated assignments (where AI supports learning) and AI-Resistant assignments (where AI would short-circuit it). That binary helped faculty act quickly and see that both stances are pedagogically valid.

But the landscape has shifted dramatically. Five developments drove the need for ARAD 2.0:

What Changed Between Versions

The evolution from ARAD 1.0 to 2.0 touches every part of the framework:

The Theoretical Foundation: Why This Framework Is Defensible

ARAD 2.0 is grounded in five established bodies of learning science research. This grounding matters: it makes the framework defensible, publishable, and transferable across disciplines and institutions. It also means that when a skeptical colleague asks "Why should I do this?" you have answers rooted in decades of evidence, not just practitioner intuition.

1. Backward Design (Wiggins & McTighe, 2005)

The GOALS process begins with desired learning outcomes and works backward to assessment and instruction. This is the same logic as Understanding by Design: identify what students should understand, determine acceptable evidence, then plan learning experiences. In an AI context, Backward Design forces the question: What human capability am I actually trying to develop? If you cannot answer that question clearly, no amount of AI policy will save the assignment.

2. Self-Regulated Learning (Zimmerman, 2002; Winne & Hadwin, 1998)

Self-Regulated Learning describes how effective learners plan, monitor, and evaluate their own cognitive processes. ARAD 2.0 builds SRL into the Architecture step by requiring metacognitive checkpoints: students must plan their AI use, monitor its quality, and evaluate what they learned through the interaction. This transforms AI from a shortcut into a metacognitive exercise. When students have to articulate why they prompted the AI a certain way and what they learned from the output, the AI interaction itself becomes a learning event.

3. Cognitive Load Theory (Sweller, 1988)

Cognitive Load Theory distinguishes between extraneous load (unnecessary processing), intrinsic load (the inherent difficulty of the material), and germane load (the productive effort that builds schemas). The AI-Limited mode is grounded in CLT: AI handles extraneous load (formatting, grammar, organization) so students can direct more cognitive resources toward germane load (the actual learning target). The key insight is that reducing the right kind of load enhances learning, while reducing the wrong kind eliminates it.

4. Evaluative Judgment (Tai et al., 2018)

Evaluative Judgment is the capability to make defensible judgments about the quality of one's own work and others'. ARAD 2.0 treats this as the central human skill in an AI-rich world. When students evaluate AI output—identifying flaws, assessing reasoning quality, comparing against disciplinary standards—they are exercising evaluative judgment. This is a higher-order capacity that AI cannot replicate because it requires the student to hold and apply quality standards, not just generate text.

5. Desirable Difficulties (Bjork & Bjork, 2011)

Desirable Difficulties theory holds that certain kinds of productive struggle—spacing, interleaving, retrieval practice, generation—enhance long-term retention even though they slow initial performance. ARAD 2.0 uses this principle as a design filter: if AI removes the struggle that produces durable learning, the assignment must be redesigned to preserve it. The goal is not to make learning harder for its own sake, but to protect the specific cognitive effort that builds lasting understanding.

The Six Core Principles

The original framework had six commitments and four design filters with significant overlap between them. ARAD 2.0 consolidates these into six unified Core Principles. Each one serves as both a design commitment and a quality filter. When you are designing or evaluating an assignment, run it through all six. If it fails any one of them, redesign.

The Three Assignment Modes

ARAD 2.0 replaces the original binary (Integrate vs. Resist) with three clearly defined modes. The choice of mode is determined by the learning goal established in Step 1 of the GOALS process, not by a blanket department or institutional policy. No single mode is inherently better. The right mode is the one that best serves the target learning.

Mode 1: AI-Integrated

AI is openly and intentionally used as part of the learning process itself. Students interact with AI to generate, critique, compare, revise, or extend their thinking—but they must demonstrate evaluative judgment throughout.

Best for learning goals that involve: evaluating and critiquing arguments or sources, synthesizing multiple perspectives, strategic decision-making, comparative analysis, revision and iterative improvement, and working with feedback.

Required safeguards:

• Full transparency (prompt logs, interaction transcripts, decision rationales)
• Structured verification tasks (not just "reflect on AI use")
• Rubric criteria that assess the student's judgment, not the AI's output
• At least one human performance checkpoint (oral defense, live explanation, or in-class application)

Mode 2: AI-Limited

AI is permitted only for bounded support tasks that reduce extraneous cognitive load. The core intellectual performance remains entirely the student's own work. This is the middle ground that ARAD 1.0 lacked, and it is where a huge number of real-world assignments naturally fall.

Best for learning goals that involve: original argument construction, creative composition or design, independent research and source evaluation, building procedural fluency, and developing personal voice or perspective.

Permitted AI uses include: grammar and spelling support, formatting assistance, brainstorming or idea clustering (with the requirement to move beyond AI suggestions), translation support for multilingual students, and clarifying assignment instructions.

Prohibited AI uses include: generating arguments, analyses, or conclusions; writing substantial portions of the deliverable; conducting the core research or evaluation; and producing the creative or intellectual heart of the work.

Required safeguards:

• Clear boundary statement explaining what is and is not permitted and why
• Brief AI use disclosure (what tools were used and for what purpose)
• Rubric criteria focused on the quality of the student's independent thinking

Mode 3: AI-Restricted

AI is not permitted because the assignment is directly measuring the student's independent cognitive performance. The point of the task is the human act itself.

Best for learning goals that involve: demonstrating mastery of foundational knowledge, performing a skill under authentic conditions, showing independent reasoning without support, building confidence in one's own capability, and providing a transfer checkpoint for high-stakes outcomes.

Task types: timed in-class writing or problem solving, oral presentations, defenses, or vivas, live demonstrations or performances, Socratic discussion or structured debate, lab technique demonstrations, and whiteboard explanations or concept mapping under observation.

Required safeguards:

• Clear explanation to students of why AI is restricted for this task (pedagogical rationale, not punishment)
• Accessible alternatives for students with disabilities
• Design that does not disadvantage students based on language background or test anxiety

The GOALS Process: Five Steps to Intentional Design

The GOALS mnemonic provides an actionable, step-by-step process for redesigning any assignment. ARAD 2.0 refines each step with sharper questions, concrete tools, and stronger theoretical grounding. Here is how to use it.

Step 1: G — Goal (Define the Human Learning Target)

Before choosing a mode or redesigning anything, identify the specific human capability you want students to develop. This is your North Star. Every subsequent decision follows from it.

Key questions to ask yourself:

• What must the student know, do, or judge by the end of this assignment?
• What kind of thinking matters most here? (Analysis? Synthesis? Evaluation? Creation? Application?)
• What part of this thinking must remain human for learning to occur?
• If I removed this assignment entirely, what capability would students lose?

The output of this step should be a clear, specific statement of the human learning target. Not "Write an essay about climate policy" but "Develop the ability to evaluate competing policy arguments using evidence and construct an original, defensible position."

Goals can be skill-based (critical thinking, argumentation, problem-solving), process-based (research methodology, design thinking, iterative revision), knowledge-based (conceptual understanding, theory application, transfer to new contexts), creative (original voice, innovative solutions, artistic expression), or metacognitive (self-monitoring, strategic planning, reflective judgment).

Step 2: O — Openness (Choose the Right AI Mode)

With your goal defined, determine the appropriate level of AI involvement. Ask these four questions in sequence:

1. Does AI support or replace the target learning? If AI can perform the exact cognitive work you are trying to teach, it replaces the learning. Move toward Restricted or Limited. If AI generates raw material that students must then evaluate, synthesize, or improve, it supports the learning. Consider Integrated.

2. What specific cognitive acts must remain human? Identify the precise moments where student thinking is essential. These moments define the boundary of AI use.

3. Would AI remove a desirable difficulty? If the productive struggle is what builds the skill (wrestling with a proof, constructing an argument from scratch, debugging code), AI may eliminate the learning mechanism even while producing a correct product.

4. Can all students access and use the required AI tools effectively? If not, either provide training and access, or adjust the mode to avoid creating inequity.

The output of this step is a clear mode selection (Integrated, Limited, or Restricted) with a brief rationale explaining why this mode best serves the learning goal. Share this rationale with students—transparency about the "why" behind AI decisions builds trust and buy-in.

Step 3: A — Architecture (Design for Visible Thinking)

This is the structural heart of the framework. Architecture determines how the assignment is sequenced, checkpointed, and scaffolded so that student thinking becomes visible and assessable. ARAD 2.0 introduces a two-phase architecture model.

Phase A: Formative Architecture (Learning Phase) emphasizes low-stakes practice, feedback, and skill building. AI use tends to be more open here because students are developing capability, not yet demonstrating mastery. Components include: a proposal or planning document (shows initial thinking before AI interaction), structured AI interaction (with prompt logs and decision rationale), peer review of AI interaction logs, instructor feedback checkpoints, and metacognitive reflection logs.

Phase B: Summative Architecture (Demonstration Phase) requires students to demonstrate what they learned. AI use may be more restricted here, or if AI is still integrated, the verification and judgment requirements are higher. Components include: the final deliverable with process documentation, a verification protocol, a human performance checkpoint (oral defense, live explanation, in-class application), and a transfer task (applying the learning to a new, unfamiliar context without AI support).

Three Reusable Architectural Patterns

ARAD 2.0 provides three architectural patterns faculty can adapt to almost any discipline:

Step 4: L — Look for Evidence (Align the Rubric)

The rubric must assess what you actually care about: the quality of student thinking, not the polish of the product. ARAD 2.0 provides six rubric dimensions, each with four performance levels (Exemplary, Proficient, Developing, Beginning). You do not need to use all six for every assignment—select the three or four most relevant to your learning goal.

Dimension 1: Cognitive Agency. Does the student demonstrate ownership of the intellectual work? At the Exemplary level, the student clearly drives all key decisions with AI subordinate to their judgment. At the Beginning level, the student appears to have accepted AI output with minimal engagement.

Dimension 2: Evaluative Judgment. Does the student critically assess AI output using disciplinary standards? At the Exemplary level, the student identifies specific strengths and weaknesses, corrects errors using course concepts, and demonstrates sophisticated disciplinary reasoning. At the Beginning level, the student accepts AI output uncritically.

Dimension 3: Process Evidence. Is the student's thinking process visible and documented? At the Exemplary level, there is complete, detailed documentation from initial thinking to final product. At the Beginning level, there is no visible process—only the final product.

Dimension 4: Verification Quality. Did the student substantively test, challenge, and improve AI output? At the Exemplary level, there is systematic verification against multiple sources that goes beyond surface fact-checking to evaluate logic and framing. At the Beginning level, AI output was accepted as-is.

Dimension 5: Disciplinary Judgment. Does the student apply course knowledge and disciplinary standards? At the Exemplary level, the student integrates course concepts throughout and demonstrates deep understanding. At the Beginning level, there is generic reasoning without disciplinary grounding.

Dimension 6: Transfer. Can the student perform the learning independently in a new context? At the Exemplary level, the student successfully applies learning to an unfamiliar context without AI support. At the Beginning level, the student cannot demonstrate the learning without AI—understanding appears tool-dependent.

Step 5: S — Scaffold and Study (Support AI Literacy, Then Pilot and Iterate)

ARAD 2.0 expands the original "Study" step into two linked functions: scaffolding AI literacy before the assignment and studying results after it.

Scaffold: AI Literacy Prerequisites

Before students can succeed in AI-Integrated or AI-Limited assignments, they need foundational AI literacy. Do not assume students know how to use AI effectively, ethically, or critically. Pre-assignment scaffolding should include a brief orientation on what generative AI can and cannot do, prompt engineering basics, verification techniques, bias awareness, privacy and data considerations, and a clear explanation of the assignment's AI mode with the pedagogical reasoning behind it.

This can be delivered as a 15-minute in-class orientation, a short self-paced module, a practice assignment with AI before the graded task, or guided examples showing strong vs. weak AI interaction.

Study: Pilot, Gather Data, Revise

After the assignment runs, gather evidence about whether the design achieved its purpose. Collect student performance data (rubric score distributions), review process artifacts (prompt logs, decision rationales), gather targeted student feedback ("What did you learn because of AI, not just with AI?"), look for metacognitive indicators, and run an equity check to see if any student group struggled with access or tool availability.

Then ask the hard revision questions: Did the task elicit the target thinking? Did students over-rely on AI despite the design safeguards? Which rubric dimensions actually captured learning? Were the AI literacy scaffolds sufficient? What surprised you about how students used AI?

ARAD 2.0 in Practice: Four Redesign Examples

Theory is necessary but insufficient. Here are four fully worked examples showing how ARAD 2.0 transforms traditional assignments that AI has rendered vulnerable.

Example 1: Composition / Essay Writing (AI-Integrated, AI Sandwich Pattern)

Traditional assignment: "Write a 1,500-word essay analyzing the effectiveness of current climate change policy."

Why it is vulnerable: AI can generate this in seconds. The assignment evaluates a product, not student thinking.

ARAD 2.0 Redesign:

Phase A (Formative): Students write a 300-word position statement on climate policy before using AI, establishing a baseline of their own thinking. Then they use AI to generate three well-reasoned counterarguments to their position, submitting the complete transcript with prompts. Peer review follows: students exchange AI transcripts and evaluate the quality of each other's prompts and the AI's responses.

Phase B (Summative): Students write a 1,000-word critical analysis identifying which AI counterarguments are valid and which contain flaws, using evidence from course readings. They explain what the AI got right, what it got wrong, and why. They then write a 300-word metacognitive reflection on what engaging with counterarguments changed about their thinking. Finally, the transfer checkpoint: 15-minute in-class timed writing responding to a new policy question using the analytical skills practiced in the assignment.

What you assess: Evaluative judgment, original argumentation, use of course evidence, metacognitive awareness, and transfer.

Example 2: Biology Lab Report (AI-Limited, Verification Challenge Pattern)

Traditional assignment: "Write a standard lab report with Introduction, Methods, Results, Discussion."

Why it is vulnerable: AI can write formulaic lab reports. A generic Methods section does not prove a student understands scientific precision.

ARAD 2.0 Redesign:

Phase A (Formative): Students conduct the lab and take detailed procedural notes by hand during the session (AI-Restricted for this step). Then they use AI to draft a Methods section based on their notes (AI-Limited: AI handles writing mechanics only).

Phase B (Summative): Students annotate the AI draft, marking at least five places where the AI's description is too vague, inaccurate, or non-replicable. For each annotation, they explain why the detail matters for replication and provide the correct information from their actual procedure. They revise the Methods section incorporating their corrections, submit a brief AI use disclosure, and in the next lab session, write a Methods section from scratch without AI support as the transfer checkpoint.

What you assess: Scientific methodology, precision of procedural description, ability to distinguish between generic and replicable writing, and independent performance.

Example 3: Business / Marketing (AI-Integrated, Comparative Analysis Pattern)

Traditional assignment: "Create a comprehensive marketing plan for a product of your choice."

Why it is vulnerable: AI can generate professional-looking marketing plans. You cannot determine whether students understand strategic decision-making.

ARAD 2.0 Redesign:

Phase A (Formative): Students select a product and write a brief strategic analysis identifying key market challenges (pre-AI baseline). They then generate three different AI marketing strategies using different prompts, submitting all prompts and outputs. Peer review follows: students exchange strategies and provide initial evaluations using Porter's Five Forces.

Phase B (Summative): Students create a comparative matrix evaluating all three AI strategies against Porter's Five Forces. They write a 1,500-word executive memo defending which strategy they would implement and why, using market data to argue against the rejected options. The transfer checkpoint: a 10-minute oral defense where students present their recommendation and respond to questions about their strategic reasoning.

What you assess: Strategic analysis, application of analytical frameworks, evidence-based argumentation, business judgment, and ability to defend decisions under questioning.

Example 4: History (AI-Integrated, Verification Challenge Pattern)

Traditional assignment: "Write a 10-page research paper analyzing the causes and consequences of the American Civil War."

Why it is vulnerable: AI can generate historically accurate, well-structured papers. This evaluates a product, not historical thinking.

ARAD 2.0 Redesign:

Phase A (Formative): Students use AI to generate a detailed timeline of key Civil War events with causal explanations, submitting the full transcript. They then identify three events or causal relationships the AI overlooked, misinterpreted, or oversimplified, using primary sources to support their critique.

Phase B (Summative): Students write a 2,000-word analytical essay arguing why the AI's omissions or misinterpretations matter for understanding Civil War causation. They must demonstrate historiographic thinking: how do different framings of evidence lead to different historical conclusions? They write a methodological reflection on what this exercise reveals about how AI handles historical complexity, causation, and contested interpretation. The transfer checkpoint: in-class document analysis applying the same critical skills to new primary sources without AI support.

What you assess: Historical thinking, source evaluation, understanding of causation and complexity, ability to identify gaps in narratives, and independent analytical performance.

The AI Literacy Primer: Preparing Students Before the Assignment

ARAD 2.0 recognizes that AI-Integrated and AI-Limited assignments require students to possess baseline AI literacy. You cannot assess evaluative judgment if students do not know how to interact with AI effectively. Here is what a recommended 30-45 minute pre-assignment module looks like:

Section 1: What Generative AI Is and Is Not (10 minutes). Cover how large language models work at a basic level (pattern prediction, not understanding), what AI does well (fluent text generation, summarization, pattern recognition, brainstorming), what it does poorly (factual accuracy, nuanced reasoning, source verification, original insight), and why AI output sounds confident even when it is wrong.

Section 2: Effective Prompting (10 minutes). Cover how input quality affects output quality, strategies for better prompts (specificity, context, constraints, role assignment), why iterative prompting produces better results than single queries, and include a practice exercise comparing outputs from a vague prompt vs. a well-crafted one.

Section 3: Critical Evaluation of AI Output (10 minutes). Cover how to fact-check AI claims against reliable sources, how to identify logical gaps, unsupported assertions, and oversimplifications, how to recognize fabricated citations or statistics, and include a practice exercise evaluating an AI-generated paragraph for a discipline-specific topic.

Section 4: Ethics, Bias, and Privacy (5-10 minutes). Cover how AI can reflect and amplify biases present in training data, what information students should avoid sharing with AI tools, academic integrity expectations for the assignment, and the difference between using AI as a tool and passing off AI work as your own thinking.

Addressing Faculty Concerns

In every workshop, seminar, and consultation where I have presented this framework, the same concerns come up. Here are honest answers.

"This feels like giving up."

It is not surrender. It is a shift from evaluating products (which AI can generate) to evaluating thinking (which AI cannot fake). You are raising the bar, not lowering it. When students must demonstrate evaluative judgment, verification skill, and transfer, the assignment is harder and more valuable than the traditional version.

"I don't know enough about AI."

You do not need to be an AI expert. The ARAD framework gives you a structured process. Start with one assignment, try AI on it yourself first, and learn alongside your students. Many of the strongest AI-integrated assignments come from faculty who were transparent about exploring the tools together with their classes.

"Students will cheat anyway."

When the process is more valuable than the product, cheating becomes much harder and much less useful. If the assignment requires documented thinking, structured verification, metacognitive reflection, and a live performance checkpoint, there is nothing to "cheat on"—the learning is in the process, and the process is visible.

"This takes too much time."

The initial redesign requires investment. But it is less time than constantly policing AI use, investigating integrity cases, redesigning assignments that no longer work, or grading products that may not reflect student learning. The ARAD process is also reusable: once you redesign one assignment, the architectural patterns transfer to others.

"What about students who don't have access to AI tools?"

This is exactly why Principle 6 (Equity and Access) exists. For AI-Integrated assignments, provide institutional access to approved tools, offer in-class time with the tools, and ensure the AI literacy primer reaches all students. For students who cannot access tools outside class, design the AI interaction to happen during class time or provide alternative pathways.

"Should I use AI detection tools?"

No. ARAD 2.0 explicitly recommends against building grading or misconduct workflows around AI detection. The evidence shows detectors are unreliable and biased. Instead, design assignments where the process evidence, verification tasks, and human performance checkpoints make detection unnecessary. If a student's oral defense reveals they cannot explain their written work, that is a pedagogical signal, not a detection result.

Implementation Guide: How to Start

Start Small

Do not try to redesign every assignment at once. Pick one assignment where students currently struggle or seem disengaged, where you suspect AI use is already happening, where AI could genuinely enhance the learning if used well, or where the learning goal is clear enough to anchor a redesign.

Work Through GOALS Systematically

For your chosen assignment:

1. Goal: Write a one-sentence statement of the human learning target.
2. Openness: Choose a mode and write a brief rationale.
3. Architecture: Sketch the assignment sequence using one of the three patterns (AI Sandwich, Verification Challenge, or Comparative Analysis). Identify formative and summative phases.
4. Look for Evidence: Select 3-4 of the six rubric dimensions most relevant to your assignment. Write performance descriptors for at least two levels.
5. Scaffold and Study: Plan a brief AI literacy orientation. After the assignment runs, collect at least one form of data (rubric score distribution, student feedback, or process artifact review).

Build Across a Course

Once you have redesigned one assignment, consider the full course. Use a mix of all three modes across the semester. Ensure at least one AI-Restricted human performance checkpoint for each major learning outcome. Build AI literacy cumulatively: early assignments scaffold skills that later assignments require. Increase the complexity of evaluative judgment tasks as the course progresses.

Share and Collaborate

Discuss your redesign with colleagues. Share rubrics and architectural patterns across your department. Contribute to your institution's growing library of AI-responsive assignments. Report what worked and what did not—the field needs practitioner evidence.

The Road Ahead

ARAD 2.0 is designed to be durable. AI tools will continue to evolve, but the framework's core logic does not depend on any specific tool's capabilities. It depends on a set of principles about human learning that remain stable:

• Students learn by thinking, not by receiving polished output.
• Assessment must measure the thinking it claims to measure.
• Productive struggle builds durable understanding.
• Judgment, verification, and transfer are human capacities worth developing.
• Fairness requires intentional design.

The goal of ARAD 2.0 is not to eliminate AI from education. The goal is to ensure that when AI is present, human learning is still happening—visibly, substantively, and equitably. We are not trying to return to a pre-AI world. We are designing a better one: a world where students learn to think with AI while retaining the human capacities that matter most—judgment, creativity, ethical reasoning, metacognitive awareness, and the ability to know what they know and what they do not.

References

Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347-364.

Bjork, R. A., & Bjork, E. L. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher et al. (Eds.), Psychology and the real world (pp. 56-64). Worth Publishers.

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74.

Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction (pp. 453-494). Lawrence Erlbaum Associates.

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779.

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.

Scarfe, P., Watcham, K., Clarke, A., & Sherrill, A. (2024). A real-world test of artificial intelligence infiltration of a university examination system. PLOS ONE, 19(10), e0305354.

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.

Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467-481.

Wiggins, G., & McTighe, J. (2005). Understanding by Design (2nd ed.). Association for Supervision and Curriculum Development.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graeber (Eds.), Metacognition in educational theory and practice (pp. 277-304). Lawrence Erlbaum Associates.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.