AW-Module 1: Academic Integrity and Cognitive Ownership

⚡️ Building an ethical foundation for research, authorship, and responsible AI use

Academic writing begins before a student writes the first sentence of a proposal. It begins with decisions about ownership, responsibility, and the honest representation of ideas. This week establishes the ethical foundation for the entire course by examining plagiarism, cheating, unauthorized collaboration, and the growing role of generative AI in research and writing. Rather than treating integrity as a list of prohibitions, the chapter frames it as a set of habits that protect learning and make academic work trustworthy. Students will analyze difficult cases, classify acceptable and unacceptable forms of support, and begin documenting their own decisions. The goal is not merely to avoid misconduct; it is to develop cognitive ownership—the ability to explain where ideas came from, how they were evaluated, and why the final choices belong to the writer.

Essential understanding(s)

What does it mean to be the genuine author of an academic text in an era of generative AI?
How can transparent documentation protect both academic integrity and student learning?

Guiding Questions

How do plagiarism, cheating, and unauthorized collaboration differ?
Which uses of GenAI support planning without replacing reading, writing, or thinking?
What evidence can demonstrate cognitive ownership of a research topic?
How should an AI-use log record prompts, decisions, and rejected suggestions?

Learning outcomes

Distinguish among academic dishonesty, cheating, plagiarism, and unauthorized collaboration.
Classify AI-use scenarios as authorized, unauthorized, or requiring instructor clarification.
Explain cognitive ownership, transparency, authentic voice, and originality in relation to academic writing.
Create an initial research-proposal integrity checklist, authorship declaration, and AI-use log.
Apply integrity principles to a case involving plagiarism and contested authorship.

Key concepts and vocabulary

Term	Working definition
Academic integrity	Consistent ethical conduct in learning, research, writing, and assessment.
Plagiarism	Presenting another person’s words, ideas, structure, or work as one’s own without appropriate acknowledgment.
Cheating	Using prohibited assistance, materials, or methods to gain an unfair academic advantage.
Unauthorized collaboration	Working with another person beyond the level of cooperation permitted for an assignment.
Attribution	Clearly identifying the source of words, ideas, data, or assistance.
Cognitive ownership	The writer’s demonstrated responsibility for understanding, evaluating, and making the decisions represented in the text.
Authentic voice	Language and reasoning that genuinely reflect the writer’s knowledge, choices, and current level of English.
Transparency	Openly documenting relevant assistance, processes, and sources.
Originality	The degree to which the submitted work reflects the student’s own writing and intellectual contribution.
AI Appendix	A record of prompts, outputs, decisions, revisions, and disclosures connected to approved AI use.

Main chapter content

Integrity as a practice, not a punishment

Academic integrity is often introduced through rules and penalties, but its deeper purpose is to make learning visible and scholarship dependable. A reader should be able to trust that a student understands the claims being made, has represented sources accurately, and has completed the intellectual work required by the assignment. Integrity therefore involves both what a writer does and what a writer can explain. A paper may contain correct citations and still fail to demonstrate ownership if the student cannot discuss the argument, define the central concepts, or justify the organization. Conversely, an imperfect draft can show strong integrity when the student’s reasoning, source use, and revision decisions are transparent.

Four categories require careful distinction. Plagiarism concerns unacknowledged borrowing of language, ideas, organization, or other intellectual work. Cheating involves prohibited methods or assistance used to gain an unfair advantage. Unauthorized collaboration occurs when people work together beyond the limits established by the instructor. Academic dishonesty is the broader category that includes these behaviors and other forms of deception. The boundaries depend partly on the assignment. Discussing a topic with a peer may be encouraged during brainstorming but prohibited during an individual examination. Ethical action therefore requires students to read the task instructions, ask when uncertain, and avoid assuming that a tool or practice is acceptable simply because it is available.

Authorship and cognitive ownership

Authorship is more than placing a name on a title page. In this course, authorship means accepting responsibility for the ideas, evidence, language, and decisions in the submitted work. Cognitive ownership is demonstrated when students can describe how they moved from a broad interest to a researchable topic, why they selected particular sources, how they interpreted evidence, and what they changed during revision. This concept is especially important for English language learners because polished language can sometimes hide a lack of understanding. The goal is not to eliminate every non-standard expression. A developing academic voice may include moments of awkwardness, but it should remain comprehensible, purposeful, and recognizably connected to the writer’s own thinking.

Cognitive ownership can be supported through process evidence. Notes, topic maps, handwritten drafts, audio brainstorming, source-evaluation records, version histories, and reflective explanations show how thinking developed. These artifacts are not busywork when they help the student reconstruct decisions. They become a learning trace: evidence of how the writer generated, tested, rejected, and refined ideas. A strong authorship declaration can summarize this process by identifying which parts were completed independently, what forms of feedback were used, and how the writer verified the final content.

Responsible and irresponsible uses of generative AI

Generative AI can support learning, but it can also replace the very processes the course is designed to develop. The central question is not whether AI was used, but what cognitive work the student retained. Approved planning uses may include generating possible search terms, comparing ways to narrow a topic, or mapping relationships among concepts. These uses should remain exploratory. The student must evaluate the suggestions, verify any factual information, and make the final decisions. In contrast, asking AI to write the proposal, generate source-based paragraphs that the student has not researched, or paraphrase material to disguise borrowing would undermine authorship and violate course expectations.

A useful decision test contains four questions. First, does the assignment permit this use? Second, does the tool support a learning process or replace it? Third, can the student verify the output using reliable sources and personal understanding? Fourth, can the student document the use honestly? When any answer is uncertain, the correct action is to pause and ask the instructor. Transparency does not automatically make an unauthorized practice acceptable, but it prevents concealment and allows the instructor to guide the process before a problem becomes misconduct.

Learning from an integrity case

Case analysis helps students understand that plagiarism is not always a simple matter of copied sentences. The Nuñez Arancibia case, assigned in the weekly plan, invites attention to authorship, public accusation, evidence, and the consequences of contested intellectual ownership. When reading the case, students should separate observable facts from interpretations. Who produced the original material? What similarities are documented? What explanations are offered? Which claims are supported, and which remain uncertain? This disciplined reading prevents the class from reducing a complex situation to personal opinion.

The case should then be transferred to the research-proposal context. Students can ask how similar problems might arise when a proposal includes borrowed wording, unattributed ideas, fabricated references, AI-generated text, or excessive assistance from a peer. A practical integrity checklist should address the entire process: topic ownership, source documentation, citation, collaboration, AI use, and final verification. The checklist becomes useful only when each item prompts an action. For example, “All sources are cited” is weaker than “I compared every in-text citation with the reference list and verified that each source exists.”

Documenting decisions from the first week

The AI-use log begins now, even if a student chooses not to use AI. Each entry should record the date, the task, the exact prompt or type of assistance, a brief description of the output, and the student’s decision. The decision column is essential: students should state what they kept, changed, or rejected and why. A rejected suggestion can provide strong evidence of critical thinking when the explanation identifies an inaccuracy, a mismatch with the local context, or a conflict with the assignment. The log should not become a collection of screenshots without interpretation. It is a record of judgment.

This week’s evidence of learning includes the academic-integrity diagnostic, case analysis, integrity checklist, authorship declaration, AI-use classification activity, AI-use log, and a brief no-AI reflection on topic ownership. Together, these items establish a baseline for the course. They also communicate an important principle: ethical academic writing is not achieved through a final originality score alone. It is achieved through understandable reasoning, accurate attribution, authorized support, transparent documentation, and a final text that the student can confidently explain and defend.

From concepts to this week’s deliverable

This week’s assignments should be treated as a connected set of authorship records rather than as separate compliance exercises. The integrity diagnostic helps each writer identify what they already understand about plagiarism, unauthorized collaboration, source use, and AI-supported work. The Nuñez Arancibia case analysis then moves those ideas into a concrete situation in which students must distinguish what was borrowed, what was concealed, and what responsibilities belonged to the writer. The proposal integrity checklist turns the discussion forward: before choosing a topic or collecting sources, students decide how they will preserve ownership of their reasoning. The authorship declaration, AI-use log, and no-AI topic-ownership reflection provide visible evidence of those decisions. Together, the documents establish a baseline against which later drafting and revision can be evaluated.

A useful test throughout the semester is whether the student can explain the origin and function of every important choice in the text. A writer should be able to answer questions such as: Why did I select this topic? How did I locate this source? What did I understand before consulting an AI tool? Which suggestion did I reject, and why? Where is my own interpretation visible? These questions do not prohibit assistance; they keep assistance subordinate to learning. When students document prompts and decisions from the beginning, the record becomes more than proof of compliance. It becomes a learning trace showing how ideas developed, how judgment improved, and how the final literature review remained human-led. This first week therefore establishes the ethical and procedural habits that support every later chapter.

Suggested weekly learning sequence

A productive learning sequence for this chapter begins with a diagnostic response to the essential question, “What does it mean to be the genuine author of an academic text in an era of generative AI?” Students should first record an individual answer and then compare it with examples, course criteria, and peer interpretations. A short concept-sorting activity using Academic integrity, Plagiarism, Cheating, and Unauthorized collaboration can make important distinctions visible before students work on a full draft. The instructor can then model one decision aloud, showing not only the completed example but also the questions, alternatives, and evidence that led to it. Students apply the same process to a small portion of their own project and receive formative feedback before extending the work independently.

The second part of the week should move from practice to production. Students use the guiding questions—“How do plagiarism, cheating, and unauthorized collaboration differ?” and “Which uses of GenAI support planning without replacing reading, writing, or thinking?”—as checkpoints while working toward the stated outcomes, especially the ability to distinguish among academic dishonesty, cheating, plagiarism, and unauthorized collaboration and to classify AI-use scenarios as authorized, unauthorized, or requiring instructor clarification. A brief peer conference should focus on one high-impact feature rather than attempting to correct everything at once. Students then revise, annotate the evidence of their decision-making, and complete the week’s deliverable: Integrity diagnostic; Nuñez Arancibia case analysis; proposal integrity checklist; authorship declaration; AI-use log; no-AI topic-ownership reflection. The final five minutes of class can be used for an exit reflection identifying one decision the writer can now justify and one question that must be carried into the next chapter.

Schedule

🗓️ Wednesday (11:00 AM - 1:00 PM)

Review ICTs.
1. Capacities
2. Microsoft Teams: l8gtqgy
Review syllabus.
Learner Expectations and Course Readiness Survey – Fill out form
Update/add profile picture in Teams.

🗓️ Thursday (12:00 PM - 1:00 PM)

Compare and Choose Matrix - Do this if you are considering different problems.

Purpose: Use this organizer when you have several possible ideas and need to compare them in order to choose the strongest researchable problem.

Criteria	Idea 1	Idea 2	Idea 3
Topic/issue
Area (Applied Linguistics / SLA / both)
Who is affected?
In what context?
What is the actual problem?
Is the problem observable? How?
Why is it important?
Is it researchable in a realistic way?
Could I find data or participants?
What concepts or theories might connect to it?
What makes this idea interesting?
Possible research question

Reflection questions

After completing the matrix, answer:

Which idea seems the most important?

Which idea seems the most feasible to research?

Which idea has the clearest context and population?

Which idea has the strongest potential as a researchable problem?

My final choice is:

Why I chose it:

Problem Funnel - Do this if you are fairly confident with your chosen problem.

Purpose: Use this organizer to move from a broad topic area to a specific, researchable problem. Add to your writing notebook notes to each of the 10 steps below.

Step	Guidling Question	Your Notes
1. Broad area of interest	What general area in applied linguistics or SLA interests you?
2. Specific theme	What more specific issue within that area interests you?
3. Learning/teaching context	In what context does this issue appear? (e.g., university EFL classroom, bilingual school, online learning, adult learners, teacher education)
4. Learner/population	Who is affected by this issue?
5. Observable problem	What is happening that suggests there is a problem?
6. Evidence of the problem	What examples, observations, or experiences suggest this is a real issue?
7. Why it matters	Why is this problem important for learners, teachers, or institutions?
8. What is not fully understood	What part of this problem still needs explanation or investigation?
9. Draft researchable problem	Write the problem as one clear statement.
10. Possible research question	What question could guide your investigation?

Sentence starters

• A broad area I am interested in is ______________________________.
• A more specific issue within this area is ______________________________.
• This problem seems to affect ______________________________ in ______________________________.
• I know this is a problem because ______________________________.
• This problem matters because ______________________________.
• A researchable problem may be: ______________________________.
• One possible research question is: ______________________________.

🗓️ Friday (10:00 AM - 12:00 PM)

🗓️ Which are researchable problems and which are not?

Read each problem below. Decide whether it is:
- G = Too General for Research
- R = More Researchable
For each problem, be prepared to explain why you classified it that way: population or participants, phenomonon, context, condition, type of evidence?
1. B1-level university EFL students who use generative AI to revise academic paragraphs sometimes accept vocabulary substitutions that they cannot explain or use independently in subsequent writing.
  Classification: _______
2. Motivation influences second-language learning.
  Classification: _______
3. Spanish-speaking A2–B1 EFL learners frequently have difficulty distinguishing and producing the English /ɪ/ and /iː/ vowel contrast in high-frequency words such as ship/sheep and live/leave**.**
  Classification: _______
4. Second-language learners have difficulty learning vocabulary.
  Classification: _______
5. Students have difficulty participating in classroom discussions in a second language.
  Classification: _______
6. Intermediate university EFL learners who receive indirect written corrective feedback on article use (a, an, the) continue to repeat the same article errors across multiple writing assignments.
  Classification: _______
7. Social media affects how learners use and learn English.
  Classification: _______
8. A2-level university EFL learners have difficulty identifying specific details in 2–3 minute authentic conversations when speakers use natural speech phenomena such as contractions, reductions, and connected speech.
  Classification: _______
9. Teachers use different kinds of corrective feedback in language classrooms.
  Classification: _______
10. First-semester university EFL learners who report moderate-to-high speaking anxiety participate less frequently in spontaneous small-group discussions than classmates reporting lower anxiety.
  Classification: _______
11. Students’ first language influences their second-language writing.
  Classification: _______
12. University EFL learners who regularly watch English-language short-form videos on TikTok or Instagram report frequent exposure to informal English expressions but may have difficulty interpreting those expressions when they occur in unfamiliar conversational contexts.
  Classification: _______
13. Pronunciation is difficult for learners of English as a second or foreign language.
  Classification: _______
14. First-year A2-level university EFL learners frequently forget newly encountered academic vocabulary within one week of classroom instruction, even when the words have been presented through reading activities.
  Classification: _______
15. Some language learners experience anxiety when speaking a second language.
  Classification: _______
16. Spanish-speaking intermediate EFL students frequently place adjectives after nouns when writing unrehearsed English descriptions, despite demonstrating knowledge of English adjective–noun order on grammar exercises.
  Classification: _______
17. Artificial intelligence is changing how students learn languages.
  Classification: _______
18. B1-level EFL learners participate more frequently in pair and small-group discussions than in whole-class discussions, but the linguistic complexity of their contributions may differ across these interaction formats.
  Classification: _______
19. Second-language learners have problems understanding authentic spoken language.
  Classification: _______
20. B1-level university EFL students who report strong instrumental motivation to learn English do not necessarily demonstrate high levels of voluntary participation during whole-class discussions.
  Classification: _______
Follow-Up Questions

After classifying the problems, discuss:
1. What characteristics made some problems too general?
2. What information made other problems more researchable?
3. Which researchable problems clearly identify a population (or participants)?
4. Which identify a specific linguistic or learning phenomenon?
5. Which identify a particular context, condition, or type of evidence?
6. Choose one problem you classified as too general and rewrite it as a more researchable problem.
Useful Formula

Broad topic → Population + Specific problem/phenomenon + Context or condition + Observable evidence
Review Problem Statement.
- I wish to learn more about… [topic] because I want to know how, why, when, where, etc… [indirect question] in order to… [significance of the problem, relevance, big picture].
Add your research problem as one complete sentence and your problem statement to Academic Writing Matrix.xlsx.
Review on your own researchable problems

A. Five General Problems

These are broad areas of concern that are important in applied linguistics and second language acquisition (SLA), but they are too general to investigate directly without further narrowing.
1. Limited opportunities for second-language learners to use the target language outside the classroom.
2. Difficulty developing listening comprehension in a second language.
3. The influence of first-language knowledge on second-language learning and production.
4. Differences in learner motivation and engagement in second-language classrooms.
5. Challenges associated with the use of artificial intelligence and digital technologies in second-language learning.
B. Five Researchable Problems

These problems are more specific, bounded, and empirically investigable. Each identifies a population, linguistic or learning phenomenon, and potential context for data collection.
1. A1–A2 university EFL learners have difficulty identifying key information in short authentic spoken interactions, even after completing classroom listening-comprehension activities.
  - Possible focus: listening strategies, speech rate, vocabulary knowledge, or pre-listening activities.
2. Intermediate EFL learners frequently transfer Spanish word order and grammatical patterns into their English academic writing.
  - Possible focus: particular structures such as adjective placement, subject omission, or sentence organization.
3. University EFL learners who report high motivation to learn English do not necessarily participate frequently in spontaneous classroom interaction.
  - Possible focus: relationship among motivation, willingness to communicate, language anxiety, and classroom participation.
4. EFL learners who use generative AI for writing revision may accept AI-generated linguistic changes without being able to explain why those changes improve or alter their original text.
  - Possible focus: metalinguistic awareness, critical AI literacy, revision decisions, or cognitive ownership.
5. Pre-listening prediction activities may affect how accurately A2–B1 university EFL learners comprehend short authentic conversations between English speakers.
  - Possible focus: comparing comprehension performance with and without prediction activities.
General Problem → Researchable Problem

A useful distinction is:

General problem:

EFL students have difficulty understanding spoken English.

Researchable problem:

A2-level first-year university EFL learners have difficulty identifying specific information in 2–3 minute authentic conversations spoken at a natural rate.

The second version is researchable because it identifies who, what skill, what type of language input, and the context or conditions under which the problem occurs.

Benjamin L. Stewart

Explorer