---
scenario: Major Revision decision, 5 items including a DA-CRITICAL, one item marked DELIBERATE_LIMITATION
mode: revision (within pipeline Stage 4)
demonstrates: Revision tracking, Response to Reviewers, and re-review leading to Accept
---

# Revision Recovery Example

This example shows how the revision process handles a Major Revision decision with 5 revision items, including a DA-CRITICAL finding and a deliberate limitation. It demonstrates the complete revision workflow from the Revision Roadmap through the Response to Reviewers letter, culminating in a re-review verdict of Accept with Minor Edits.

## Context

**Paper**: "Augmenting Academic Advising: How AI-Driven Recommendation Systems Reshape Student Pathway Decisions in Research Universities"
**Word count**: 8,400 words (IMRaD)
**References**: 52 entries (APA 7.0)
**Pipeline state**: Stage 2.5 (INTEGRITY) passed, Stage 3 (REVIEW) completed

---

## Starting Point: Stage 3 Review Decision

### Reviewer Configuration

```
Paper domain: Higher Education Technology / Student Affairs
Research paradigm: Post-positivism (mixed methods)
Method type: Quantitative (quasi-experiment) + Qualitative (semi-structured interviews)

Reviewer Configuration:
  EIC:         Journal of Higher Education editor, specializing in student success and retention
  Reviewer 1:  Quantitative methodologist, specializing in quasi-experimental design and causal inference
  Reviewer 2:  Higher education technologist, specializing in AI applications in student services
  Reviewer 3:  Sociologist of education, specializing in equity and access in higher education
  Devil's Advocate: Challenges core premise and identifies overlooked counter-evidence
```

### Five-Dimension Scores (Pre-Revision)

| Dimension | Weight | R1 | R2 | R3 | DA | Weighted Avg |
|-----------|--------|----|----|----|----|-------------|
| Originality | 20% | 72 | 78 | 70 | 65 | 71.3 |
| Methodological Rigor | 25% | 55 | 62 | 58 | 50 | 56.3 |
| Evidence Sufficiency | 25% | 60 | 65 | 55 | 48 | 57.0 |
| Argument Coherence | 15% | 68 | 72 | 62 | 55 | 64.3 |
| Writing Quality | 15% | 75 | 78 | 74 | 72 | 74.8 |
| **Weighted Total** | -- | -- | -- | -- | -- | **62.4** |

Per the quality rubrics (50-64 = Major Revision), this score yields:

**Editorial Decision: Major Revision**

---

### Revision Roadmap

| # | Description | Reviewer | Type | Priority | Target Section |
|---|-------------|----------|------|----------|---------------|
| 1 | Sample limited to 3 universities — insufficient for generalizable claims about "research universities" as a category. Selection criteria unclear. | R1 | Major | must_fix | Methodology (Section 3) |
| 2 | Missing discussion of competing framework: Transformative Learning Theory (Mezirow, 2000) and Self-Determination Theory (Ryan & Deci, 2020) are directly relevant but absent from the theoretical grounding. | R2 | Major | must_fix | Literature Review (Section 2) |
| 3 | Core premise "AI replaces human judgment in advising" is a strawman — no serious scholar or practitioner claims this. The paper argues against a position no one holds, which weakens the entire contribution. | DA | DA-CRITICAL | must_fix | Introduction (Section 1) + Discussion (Section 5) |
| 4 | Table 3 reports regression coefficients without confidence intervals; Table 5 reports chi-square values without effect sizes (Cramer's V). Incomplete statistical reporting. | R1 | Minor | should_fix | Results (Section 4) |
| 5 | Several paragraphs in the Discussion exceed 200 words, making them difficult to parse. APA 7.0 style guide recommends paragraph lengths that support readability. | R3 | Minor | consider | Discussion (Section 5) |

---

## Revision Tracking Template (Filled)

### Paper Information

| Field | Value |
|-------|-------|
| Paper Title | Augmenting Academic Advising: How AI-Driven Recommendation Systems Reshape Student Pathway Decisions in Research Universities |
| Revision Round | 1 |
| Date | 2026-03-06 |
| Previous Decision | Major Revision |
| Target Journal | Journal of Higher Education |
| Original Word Count | 8,400 words |
| Revised Word Count | 10,150 words |

### Revision Tracking Table

| # | Issue Description | Reviewer | Type | Section | Resolution Summary | Location of Change | Status | Reason (if not resolved) |
|---|-------------------|----------|------|---------|-------------------|-------------------|--------|--------------------------|
| 1 | Sample limited to 3 universities; selection criteria unclear; generalizable claims unsupported | R1 | Major | Methodology | Expanded sample from 3 to 8 universities via secondary dataset integration; added explicit selection criteria (Carnegie R1/R2, geographic distribution, enrollment size bands); softened generalizability claims | Section 3.1 (para 1-3), Section 3.2 (new subsection), Section 5.3 (limitations) | RESOLVED | -- |
| 2 | Missing Transformative Learning Theory and Self-Determination Theory frameworks | R2 | Major | Literature Review | Added 2-page section (Section 2.3) integrating TLT and SDT; connected both frameworks to AI-mediated advising through autonomy, competence, and relatedness constructs; added 5 new references | Section 2.3 (new, 850 words), Section 5.1 (para 3-4, theoretical integration) | RESOLVED | -- |
| 3 | "AI replaces human judgment" is a strawman; no one claims this | DA | DA-CRITICAL | Introduction + Discussion | Completely reframed from "replacement vs augmentation" binary to "augmentation spectrum" model; replaced strawman with genuine scholarly tension between algorithmic efficiency and relational advising | Section 1 (para 3-5 rewritten), Section 5.1 (para 1-2 rewritten), Section 5.4 (new synthesis paragraph) | RESOLVED | -- |
| 4 | Tables 3 and 5 missing confidence intervals and effect sizes | R1 | Minor | Results | Added 95% CIs to all regression coefficients in Table 3; added Cramer's V to all chi-square tests in Table 5; added footnotes explaining effect size interpretation | Table 3 (reformatted), Table 5 (reformatted), Table footnotes | RESOLVED | -- |
| 5 | Discussion paragraphs exceed 200 words; readability concern | R3 | Minor | Discussion | Most paragraphs restructured to 150-190 words. Three paragraphs retained at 210-220 words where splitting would disrupt a sustained argument with multiple evidence sources. | Section 5.1 (para 2: 215 words), Section 5.2 (para 4: 218 words), Section 5.4 (para 1: 212 words) | DELIBERATE_LIMITATION | These three paragraphs develop complex arguments integrating 3-4 sources each. Splitting them would require artificial transitions that weaken argumentative coherence. APA 7.0 provides a readability guideline, not a strict rule. The 10-20 word excess is marginal, and we prioritize argument integrity. Noted in Response to Reviewers with justification. |

### Summary Statistics

| Metric | Count |
|--------|-------|
| Total items | 5 |
| Resolved | 4 |
| Deliberate Limitation | 1 |
| Unresolvable | 0 |
| Reviewer Disagree | 0 |
| Word count change | +1,750 words |
| New references added | 8 |
| New figures/tables added | 0 (2 tables reformatted) |

---

## Detailed Revisions

### Item 1 (RESOLVED): Sample Expansion and Selection Criteria

**R1's concern**: "The study draws conclusions about 'research universities' as a category based on data from only three institutions. The selection criteria for these three are not stated. Were they convenience samples? If so, the paper cannot claim that findings generalize to research universities broadly."

**Changes made**:

*Section 3.1 — Before:*
> Data were collected from three research universities that had implemented AI-driven advising recommendation systems between 2022 and 2024. A total of 1,247 students participated in the quasi-experimental design.

*Section 3.1 — After:*
> Data were drawn from eight research universities classified as Carnegie R1 (n = 5) or R2 (n = 3) that had implemented AI-driven advising recommendation systems between 2022 and 2024. Institutions were selected using stratified purposive sampling across three dimensions: geographic region (Northeast: 2, Midwest: 2, South: 2, West: 2), enrollment size (large >30,000: 3, medium 15,000-30,000: 3, small <15,000: 2), and years since AI system deployment (1-2 years: 4, 3+ years: 4). The expanded dataset comprises 3,812 students across treatment (n = 1,946) and comparison (n = 1,866) groups. Five institutions contributed primary data collected for this study, while three contributed comparable secondary data from institutional research offices under data sharing agreements (see Appendix B for IRB approval details across all sites).

*Section 3.2 (new subsection) — Added:*
> 3.2 Site Selection Rationale
>
> The eight-institution sample was designed to maximize variation along dimensions most likely to moderate the effects of AI-driven advising: institutional size (which affects advisor-to-student ratios), geographic context (which correlates with student demographics), and system maturity (which may influence adoption patterns). We deliberately excluded institutions that had implemented AI advising for less than one academic year, as initial deployment effects may confound usage patterns with novelty effects (Smith & Doe, 2023). Table 1 summarizes institutional characteristics.

*Section 5.3 (limitations paragraph) — softened generalizability:*
> While the expanded eight-institution sample improves variation along key moderating dimensions, findings should be interpreted within the context of U.S. Carnegie-classified research universities. Generalization to community colleges, liberal arts institutions, or non-U.S. systems requires further study.

### Item 2 (RESOLVED): Theoretical Framework Integration

**R2's concern**: "The paper grounds its framework in Technology Acceptance Model (TAM) and Nudge Theory, which are appropriate but insufficient. Transformative Learning Theory (Mezirow, 2000) directly addresses how students process unfamiliar pathway recommendations — through disorienting dilemmas and critical reflection. Self-Determination Theory (Ryan & Deci, 2020) explains why students accept or reject AI recommendations based on autonomy, competence, and relatedness needs. Both frameworks are standard in advising research and their absence is a significant gap."

**Changes made**:

*Section 2.3 (new, 850 words) — Added:*
> 2.3 Complementary Frameworks: Transformative Learning and Self-Determination
>
> While TAM and Nudge Theory explain the mechanics of technology adoption and behavioral influence, they do not adequately account for the cognitive and motivational processes through which students engage with AI-generated pathway recommendations. Two additional frameworks address this gap.
>
> Transformative Learning Theory (Mezirow, 1991, 2000) posits that learning occurs when individuals encounter a "disorienting dilemma" that challenges their existing meaning structures, prompting critical reflection and perspective transformation. In the context of AI-driven advising, a recommendation to pursue an unexpected academic pathway (e.g., suggesting a data science minor to a humanities major based on course performance patterns) functions as precisely such a dilemma. The student must reconcile their existing self-concept with algorithmic evidence about their capabilities, a process that Mezirow would characterize as premise reflection — questioning the assumptions underlying one's goals rather than merely adjusting strategies within fixed assumptions.
>
> Recent applications of TLT to technology-mediated educational contexts support this framing. Eschenbacher and Fleming (2020) found that digital learning environments could facilitate transformative learning when they introduced "productive dissonance" — information that challenged learners' self-assessments without undermining their agency. This maps directly onto the design challenge of AI advising systems: recommendations must be surprising enough to add value beyond what students would choose independently, yet presented in ways that preserve student autonomy over final decisions.
>
> Self-Determination Theory (SDT; Ryan & Deci, 2000, 2020) provides a motivational lens. SDT holds that intrinsic motivation depends on three basic psychological needs: autonomy (feeling volitional choice), competence (feeling capable), and relatedness (feeling connected to others). AI-driven recommendations interact with all three needs. Autonomy is threatened when algorithmic recommendations feel prescriptive; competence is supported when recommendations align with demonstrated strengths; relatedness is complicated when algorithmic mediation reduces direct advisor-student interaction.
>
> Niemiec and Ryan (2009) demonstrated that autonomy-supportive framing of recommendations — presenting options rather than directives, explaining the rationale behind suggestions — significantly increased both acceptance and satisfaction compared to controlling framing. This finding has direct design implications for AI advising interfaces. [...]

*New references added:*
- Eschenbacher, S., & Fleming, T. (2020). Transformative dimensions of lifelong learning: Mezirow, Rorty and COVID-19. *International Review of Education*, 66, 657-672. https://doi.org/10.1007/s11159-020-09859-6
- Mezirow, J. (1991). *Transformative dimensions of adult learning*. Jossey-Bass.
- Mezirow, J. (2000). Learning to think like an adult: Core concepts of transformation theory. In J. Mezirow & Associates (Eds.), *Learning as transformation* (pp. 3-34). Jossey-Bass.
- Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom. *Theory and Research in Education*, 7(2), 133-144. https://doi.org/10.1177/1477878509104318
- Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective. *Contemporary Educational Psychology*, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860

### Item 3 (RESOLVED): DA-CRITICAL Strawman Reframing

**Devil's Advocate finding**: "The paper's core framing — 'AI replaces human judgment in advising' — is a strawman. No advising professional, institutional leader, or serious EdTech scholar argues for wholesale replacement of human advisors with algorithms. The actual scholarly tension is about the *degree* of algorithmic influence on student decisions and whether AI-mediated nudges constitute legitimate guidance or subtle coercion. By arguing against a position no one holds, the paper's contribution is undermined: it triumphantly demonstrates that augmentation is better than replacement, when no one disputed this."

**This was the most significant revision.** The DA-CRITICAL finding required reframing the paper's central argument, affecting the Introduction and Discussion.

**Changes made**:

*Section 1, para 3-5 — Before:*
> A fundamental question facing higher education is whether AI can replace human judgment in academic advising. Proponents argue that algorithmic systems can process more information, identify patterns invisible to human advisors, and provide consistent guidance at scale. Critics counter that advising is inherently relational and cannot be reduced to algorithmic optimization.
>
> This study investigates whether AI-driven recommendation systems can effectively replace traditional human advising for routine pathway decisions, freeing human advisors for more complex cases.
>
> We hypothesize that AI-driven recommendations will produce equivalent or superior pathway outcomes compared to human-only advising, as measured by course completion rates, time-to-degree, and student satisfaction.

*Section 1, para 3-5 — After:*
> The scholarly debate around AI in academic advising is not about whether algorithms should replace human advisors — no serious scholar or practitioner advocates for this (Museus & Ravello, 2010; Lowenstein, 2021). Rather, the substantive tension concerns the *nature and degree* of algorithmic influence on student pathway decisions. On one end of the augmentation spectrum, AI serves as an information tool that surfaces data patterns for human interpretation. On the other end, AI systems actively shape student choices through recommendation architectures, default options, and behavioral nudges — raising questions about whether such influence constitutes legitimate guidance or a subtle form of choice architecture that constrains genuine autonomy (Thaler & Sunstein, 2021; Selwyn, 2023).
>
> This study investigates where on this augmentation spectrum AI-driven advising systems actually operate in practice, and how different degrees of algorithmic involvement affect student pathway outcomes and decision-making agency. We examine not just *whether* AI recommendations improve outcomes, but *how* they interact with students' existing decision-making processes and advisor-student relationships.
>
> We advance three hypotheses: (H1) AI-augmented advising will produce improved pathway outcomes compared to human-only advising, as measured by course completion rates and time-to-degree; (H2) the magnitude of improvement will vary by the degree of algorithmic influence in the recommendation interface; and (H3) student-reported decision-making agency will moderate the relationship between AI recommendation acceptance and satisfaction.

*Section 5.1, para 1-2 — Before:*
> Our findings confirm that AI can indeed serve as an effective replacement for human advisors in routine pathway decisions. The quasi-experimental results show significantly better outcomes in the AI-recommendation group across all three metrics. This suggests that the fears about AI replacing human advising are unfounded — at least for straightforward pathway decisions.

*Section 5.1, para 1-2 — After:*
> Our findings contribute to understanding the augmentation spectrum in AI-mediated advising rather than adjudicating a replacement-versus-augmentation binary. The quasi-experimental results show that AI-augmented advising produced improved pathway outcomes across all three metrics, but the qualitative data reveal a more nuanced picture. Students who reported high perceived autonomy over their final decisions — regardless of whether they followed the AI recommendation — showed the highest satisfaction scores. This aligns with SDT predictions (Ryan & Deci, 2020) and suggests that the *framing* of AI recommendations matters as much as their accuracy.
>
> The interaction between algorithmic influence degree and student agency (H3) proved to be the most theoretically interesting finding. High-influence interfaces (those presenting a single "recommended pathway" as a default) produced better short-term outcome metrics but lower perceived autonomy, while low-influence interfaces (those presenting multiple pathways with data-driven comparisons) produced slightly lower outcome metrics but significantly higher autonomy and satisfaction. This tension — between optimizing outcomes and preserving agency — is the genuine scholarly challenge, not the solved question of whether AI "replaces" human advisors.

*Section 5.4 (new synthesis paragraph) — Added:*
> The augmentation spectrum framework we propose moves the field beyond binary debates and toward a more productive question: what degree of algorithmic involvement is appropriate for what type of advising interaction, for which student population, at which institutional context? Our eight-institution comparison suggests that the answer is contextual — institutions with lower advisor-to-student ratios benefited more from high-influence AI interfaces, while institutions with robust advising cultures showed better outcomes with low-influence, data-presentation interfaces. This finding underscores that AI advising system design is not merely a technical challenge but a values-driven institutional decision about the proper role of algorithmic authority in student development.

### Item 4 (RESOLVED): Statistical Reporting

**R1's concern**: "Table 3 reports regression coefficients without confidence intervals, and Table 5 reports chi-square values without effect sizes. This is incomplete statistical reporting that does not meet current methodological standards (APA 7.0 Section 6.44)."

*Table 3 — Before:*

| Predictor | B | SE | beta | p |
|-----------|---|----|------|---|
| AI recommendation acceptance | 0.42 | 0.08 | .31 | < .001 |
| Prior GPA | 0.55 | 0.06 | .38 | < .001 |
| Advisor contact hours | 0.18 | 0.07 | .14 | .012 |
| Perceived autonomy | 0.29 | 0.09 | .22 | < .001 |

*Table 3 — After:*

| Predictor | B | 95% CI | SE | beta | p |
|-----------|---|--------|-----|------|---|
| AI recommendation acceptance | 0.42 | [0.26, 0.58] | 0.08 | .31 | < .001 |
| Prior GPA | 0.55 | [0.43, 0.67] | 0.06 | .38 | < .001 |
| Advisor contact hours | 0.18 | [0.04, 0.32] | 0.07 | .14 | .012 |
| Perceived autonomy | 0.29 | [0.11, 0.47] | 0.09 | .22 | < .001 |

*Note*. N = 3,812. R-squared = .34, adjusted R-squared = .33, F(4, 3807) = 487.2, p < .001. 95% confidence intervals based on bootstrap resampling (5,000 iterations).

*Table 5 — Before:*

| Comparison | chi-square | df | p |
|-----------|-----------|-----|---|
| Pathway change by condition | 18.42 | 3 | < .001 |
| Satisfaction by influence level | 12.87 | 2 | .002 |
| Retention by AI acceptance | 8.34 | 1 | .004 |

*Table 5 — After:*

| Comparison | chi-square | df | p | Cramer's V | Effect Size |
|-----------|-----------|-----|---|-----------|-------------|
| Pathway change by condition | 18.42 | 3 | < .001 | .07 | Small |
| Satisfaction by influence level | 12.87 | 2 | .002 | .06 | Small |
| Retention by AI acceptance | 8.34 | 1 | .004 | .05 | Small |

*Note*. Cramer's V interpretation follows Cohen (1988): small = .10, medium = .30, large = .50 for df* = 1. Given the large sample size (N = 3,812), statistically significant results with small effect sizes should be interpreted with attention to practical significance.

### Item 5 (DELIBERATE_LIMITATION): Paragraph Length

**R3's concern**: "Several paragraphs in the Discussion exceed 200 words. While not a strict rule violation, shorter paragraphs improve readability and signal clear argumentative structure."

**Action taken**: 14 of 17 Discussion paragraphs were restructured to 150-190 words. Three paragraphs were deliberately retained at 210-220 words:

| Paragraph | Word Count | Justification |
|-----------|-----------|---------------|
| Section 5.1, para 2 (autonomy-outcome tension) | 215 | Develops the SDT-informed argument integrating quantitative results (H3) with interview data from 3 institutions. Splitting would require an artificial transition between the statistical finding and its qualitative elaboration. |
| Section 5.2, para 4 (institutional context moderation) | 218 | Synthesizes advisor-to-student ratio data across 8 institutions with qualitative themes about advising culture. The comparison structure (high-ratio vs low-ratio institutions) requires sustained exposition. |
| Section 5.4, para 1 (augmentation spectrum synthesis) | 212 | The new synthesis paragraph responding to the DA-CRITICAL item. This is the paper's core theoretical contribution and splitting it would dilute the argument. |

**Limitations section reference**: "We acknowledge that three Discussion paragraphs exceed the 200-word readability guideline (APA 7.0 Section 3.08). These paragraphs were retained at 210-220 words to preserve argumentative coherence in passages that integrate multiple evidence sources."

---

## Response to Reviewers

---

**Manuscript No.:** JHE-2026-0287-R1

**Manuscript Title:** Augmenting Academic Advising: How AI-Driven Recommendation Systems Reshape Student Pathway Decisions in Research Universities

**Revision Date:** 2026-03-06

---

Dear Editor and Reviewers,

Thank you for your thorough and constructive review of our manuscript. The feedback has significantly strengthened the paper, particularly the Devil's Advocate challenge regarding our core framing, which prompted a substantive reorientation of the paper's argument. We respond to each item below. All revisions are marked in blue in the revised manuscript.

---

### Response to Reviewer 1

#### Comment R1-1: Sample Size and Selection Criteria (Major)

**Reviewer comment**: "The study draws conclusions about 'research universities' as a category based on data from only three institutions. The selection criteria for these three are not stated. Were they convenience samples? If so, the paper cannot claim that findings generalize to research universities broadly."

**Author response**: We agree that the original three-institution sample was insufficient for the generalizability claims made in the paper. We have addressed this in two ways: (1) expanded the dataset from 3 to 8 universities by integrating comparable secondary data from five additional institutions under data sharing agreements, and (2) added explicit stratified purposive sampling criteria across three dimensions (geographic region, enrollment size, and AI system deployment maturity). We have also softened the generalizability language throughout the paper, particularly in the Discussion and Limitations sections, to reflect the expanded but still bounded scope.

**Changes made**:
- Section 3.1, paragraphs 1-3: Rewritten to describe the 8-institution sample with selection criteria (pp. 12-13)
- Section 3.2 (new subsection): "Site Selection Rationale" explaining the stratified sampling logic (p. 14)
- Table 1: Expanded to include all 8 institutions with Carnegie classification, enrollment, region, and deployment year
- Section 5.3: Limitations paragraph revised to acknowledge scope boundaries (p. 34)

> See revised manuscript pp. 12-14, 34

#### Comment R1-2: Missing Confidence Intervals and Effect Sizes (Minor)

**Reviewer comment**: "Table 3 reports regression coefficients without confidence intervals; Table 5 reports chi-square values without effect sizes (Cramer's V). Incomplete statistical reporting."

**Author response**: We have added 95% bootstrap confidence intervals (5,000 iterations) to all regression coefficients in Table 3, and Cramer's V with effect size interpretation to all chi-square tests in Table 5. We also added interpretive footnotes noting that statistically significant results with small effect sizes (Cramer's V = .05-.07) should be interpreted with attention to practical significance given the large sample size.

**Changes made**:
- Table 3: Reformatted with 95% CI column and bootstrap footnote (p. 22)
- Table 5: Reformatted with Cramer's V and effect size interpretation columns (p. 25)
- Added footnotes to both tables with interpretation guidance

> See revised manuscript pp. 22, 25

---

### Response to Reviewer 2

#### Comment R2-1: Missing Theoretical Frameworks (Major)

**Reviewer comment**: "The paper grounds its framework in Technology Acceptance Model and Nudge Theory, which are appropriate but insufficient. Transformative Learning Theory (Mezirow, 2000) and Self-Determination Theory (Ryan & Deci, 2020) are directly relevant but absent from the theoretical grounding."

**Author response**: We fully agree that these frameworks are essential and their absence was a significant gap. We have added a new Section 2.3 ("Complementary Frameworks: Transformative Learning and Self-Determination," approximately 850 words) that integrates both TLT and SDT into the paper's theoretical foundation. Specifically, we use TLT to explain how AI pathway recommendations function as "disorienting dilemmas" that prompt premise reflection, and SDT to explain how autonomy, competence, and relatedness needs moderate student responses to algorithmic recommendations. Five new references support this section. We have also woven these frameworks into the Discussion (Section 5.1, paragraphs 3-4), connecting our empirical findings to SDT's autonomy predictions.

**Changes made**:
- Section 2.3 (new, 850 words): TLT and SDT integration with advising context (pp. 9-11)
- Section 5.1, paragraphs 3-4: Theoretical discussion linking findings to SDT autonomy construct (p. 30)
- 5 new references added: Eschenbacher & Fleming (2020), Mezirow (1991, 2000), Niemiec & Ryan (2009), Ryan & Deci (2020)

> See revised manuscript pp. 9-11, 30

---

### Response to Devil's Advocate

#### Comment DA-1: Strawman Framing (DA-CRITICAL)

**Devil's Advocate challenge**: "The paper's core framing — 'AI replaces human judgment in advising' — is a strawman. No advising professional, institutional leader, or serious EdTech scholar argues for wholesale replacement of human advisors with algorithms. The actual scholarly tension is about the *degree* of algorithmic influence on student decisions and whether AI-mediated nudges constitute legitimate guidance or subtle coercion. By arguing against a position no one holds, the paper's contribution is undermined."

**Author response**: This is the most consequential feedback we received, and we accept it fully. Upon reflection, our original framing indeed set up and argued against a position that no serious stakeholder holds. We have fundamentally reframed the paper's argument.

The original binary framing ("replacement vs augmentation") has been replaced with an "augmentation spectrum" model that positions the genuine scholarly tension along a continuum of algorithmic influence — from passive data presentation to active choice architecture. This reframing changes the paper's core contribution from demonstrating that augmentation beats replacement (a trivial claim) to investigating *how different degrees of algorithmic involvement affect both outcomes and student agency* (a substantive contribution).

Specific changes:

1. **Introduction (Section 1, paragraphs 3-5)**: Completely rewritten. The new framing explicitly acknowledges that replacement is not the live question, introduces the "augmentation spectrum" concept, and positions the paper's contribution as investigating the interaction between algorithmic influence degree and student decision-making agency. Three hypotheses have been refined to reflect this reframing.

2. **Discussion (Section 5.1, paragraphs 1-2)**: Rewritten to interpret findings through the augmentation spectrum lens. The most theoretically interesting finding — that high-influence interfaces produce better short-term outcomes but lower perceived autonomy, while low-influence interfaces produce the inverse — is now the centerpiece of the Discussion rather than an afterthought.

3. **New synthesis paragraph (Section 5.4)**: Proposes that the augmentation spectrum framework moves the field toward a more productive question: what degree of algorithmic involvement is appropriate for what advising interaction, for which student population, at which institutional context?

4. **Title unchanged**: The word "Augmenting" in the title already implies a non-replacement framing, so no title change was necessary. However, the abstract has been revised to reflect the augmentation spectrum language.

**Changes made**:
- Section 1, paragraphs 3-5: Complete rewrite (pp. 2-3)
- Section 5.1, paragraphs 1-2: Complete rewrite (pp. 29-30)
- Section 5.4: New synthesis paragraph (p. 33)
- Abstract: Revised to reflect augmentation spectrum framing (p. ii)
- 2 new references added: Lowenstein (2021), Museus & Ravello (2010)

> See revised manuscript pp. 2-3, 29-30, 33, ii

---

### Response to Reviewer 3

#### Comment R3-1: Discussion Paragraph Length (Minor)

**Reviewer comment**: "Several paragraphs in the Discussion exceed 200 words. While not a strict rule violation, shorter paragraphs improve readability."

**Author response**: We have restructured 14 of 17 Discussion paragraphs to 150-190 words. We respectfully retained three paragraphs at 210-220 words (Section 5.1 para 2, Section 5.2 para 4, Section 5.4 para 1) where splitting would disrupt sustained arguments that integrate multiple evidence sources. APA 7.0 Section 3.08 provides a readability guideline rather than a strict rule, and we believe the marginal 10-20 word excess is justified by argumentative coherence. We have documented this as a deliberate limitation in the Limitations section.

**Changes made**:
- Section 5 throughout: 14 paragraphs restructured to 150-190 words (pp. 29-35)
- Section 5.3: Added acknowledgment of retained paragraph lengths (p. 34)

> See revised manuscript pp. 29-35

---

### Summary of Changes

- Total comments addressed: 5
- Resolved: 4
- Deliberate Limitation: 1
- Word count change: +1,750 words (8,400 to 10,150)
- New references added: 8
- New figures/tables added: 0 (2 tables reformatted with additional statistical reporting)

We believe these revisions have substantially strengthened the manuscript — particularly the reframing prompted by the Devil's Advocate, which we consider the single most valuable piece of feedback. We look forward to your further evaluation.

Sincerely,

Corresponding Author: [Author Name]
Contact Email: [email]
Revision Date: 2026-03-06

---

## Stage 3': Re-Review Verdict

```
Entering Stage 3' (RE-REVIEW) -- Loop 1/2

Loading academic-paper-reviewer SKILL.md (re-review mode)...
Passing Revised Draft + Response to Reviewers + original Revision Roadmap...
5 reviewers re-reviewing revision quality...
```

### Revision Response Verification

| # | Original Issue | Response Adequate? | Detail |
|---|---------------|:------------------:|--------|
| 1 | Sample limited to 3 universities | Adequate | Expanded to 8 with clear selection criteria (stratified purposive sampling). Generalizability claims appropriately softened. New Table 1 provides full institutional profiles. Secondary data integration is methodologically sound with IRB documentation. |
| 2 | Missing TLT and SDT frameworks | Adequate | New Section 2.3 (850 words) integrates both frameworks with specific connections to AI advising context. Five new references are all peer-reviewed. SDT is further applied in Discussion Section 5.1 to interpret the autonomy-outcome tension finding. Integration is substantive, not superficial. |
| 3 | "AI replaces human judgment" strawman (DA-CRITICAL) | Adequate | Complete reframing from replacement binary to augmentation spectrum. The new framing identifies a genuine scholarly tension (algorithmic influence degree vs student agency) and makes a substantive contribution. The DA-CRITICAL item is fully resolved — the paper no longer argues against a position no one holds. |
| 4 | Missing CIs and effect sizes | Adequate | 95% bootstrap CIs added to Table 3; Cramer's V with interpretation added to Table 5. Footnotes appropriately note that small effect sizes with large samples should be interpreted cautiously. |
| 5 | Discussion paragraph length | Adequate (DELIBERATE_LIMITATION) | 14/17 paragraphs restructured. Three retained at 210-220 words with documented justification. This is a reasonable editorial decision and does not compromise the paper's quality. |

### Five-Dimension Scores (Post-Revision)

| Dimension | Weight | Pre-Revision | Post-Revision | Change |
|-----------|--------|-------------|--------------|--------|
| Originality | 20% | 71.3 | 80.5 | +9.2 |
| Methodological Rigor | 25% | 56.3 | 74.0 | +17.7 |
| Evidence Sufficiency | 25% | 57.0 | 76.5 | +19.5 |
| Argument Coherence | 15% | 64.3 | 82.0 | +17.7 |
| Writing Quality | 15% | 74.8 | 79.3 | +4.5 |
| **Weighted Total** | -- | **62.4** | **78.0** | **+15.6** |

Per the quality rubrics: 65-79 = Minor Revision, >= 80 = Accept. Score of 78.0 is in the Minor Revision range, but all Major items and the DA-CRITICAL item are fully resolved.

### DA-CRITICAL Before/After Assessment

| Aspect | Before Revision | After Revision |
|--------|----------------|---------------|
| Core framing | "AI replaces human judgment" — strawman binary | "Augmentation spectrum" — genuine scholarly tension |
| Central question | Can AI replace human advisors? (trivially answered) | What degree of algorithmic influence optimizes both outcomes and agency? (substantive) |
| Theoretical depth | TAM + Nudge Theory only | TAM + Nudge Theory + TLT + SDT (4 frameworks) |
| Discussion contribution | "Augmentation works better than replacement" (obvious) | Autonomy-outcome tension as a function of interface design and institutional context (novel) |
| DA verdict | CRITICAL — undermines entire contribution | Resolved — paper now addresses a genuine gap |

### Editorial Synthesizer Assessment

```
All 5 reviewers concur: revision quality is strong.

Key improvements:
1. The DA-CRITICAL strawman reframing is the most significant improvement.
   The paper now makes a substantive contribution rather than arguing
   against a position no one holds.
2. The 8-institution sample with stratified selection criteria addresses
   the generalizability concern raised by R1.
3. The TLT/SDT integration adds theoretical depth that was clearly missing.
4. The DELIBERATE_LIMITATION on paragraph length is reasonable and
   well-documented.

Residual suggestions (non-blocking):
S1: Consider adding a figure depicting the "augmentation spectrum"
    concept to strengthen the theoretical contribution visually.
S2: The transition between Section 2.2 and the new Section 2.3 could
    be smoother — currently the reader encounters TLT without a clear
    signpost for why it appears at that point.

Editorial Decision: Accept with Minor Edits
  - The two residual suggestions are editorial improvements,
    not substantive concerns.
  - Score moved from 62.4 (Major Revision) to 78.0 (upper Minor Revision).
  - All SERIOUS/CRITICAL items resolved.
  - Paper is cleared for finalization after minor edits.
```

---

## Pipeline Continues: Stage 3' --> Stage 4.5

```
━━━ MANDATORY CHECKPOINT: Stage 3' RE-REVIEW ━━━

Review Result: Accept with Minor Edits

Revision assessment:
  - 5/5 items addressed (4 RESOLVED, 1 DELIBERATE_LIMITATION)
  - DA-CRITICAL item fully resolved (strawman → augmentation spectrum)
  - Weighted score: 62.4 → 78.0 (+15.6 points)
  - 2 residual suggestions (non-blocking, can be addressed in finalization)

Since the decision is Accept (not Major Revision), the paper
proceeds directly to Stage 4.5 (FINAL INTEGRITY) — skipping Stage 4'.

The 2 minor editorial suggestions will be handled during
Stage 5 (FINALIZE).

Continue to Stage 4.5?

Progress: [v]Research -> [v]Writing -> [v]Integrity -> [v]Review
       -> [v]Revision -> [v]Re-review -> [..]Final Integrity
       -> [ ]Finalization
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**User**: Continue!

---

## Key Takeaways

### 1. DA-CRITICAL Findings Drive the Most Valuable Revisions

The Devil's Advocate challenge — that the paper's core premise was a strawman — prompted the most significant improvement. The original framing ("AI replaces human judgment") was a binary that no one disputed. The revised framing ("augmentation spectrum") introduces a genuine scholarly contribution. This is precisely the value of the DA-CRITICAL designation: it forces authors to confront foundational weaknesses rather than polishing surface-level issues.

### 2. DELIBERATE_LIMITATION Is a Legitimate Status

Not every reviewer suggestion must be accepted. Item 5 (paragraph length) was acknowledged as a deliberate design choice with documented justification. The key is that DELIBERATE_LIMITATION requires (a) a principled reason (argumentative coherence > readability guideline), (b) an acknowledgment in the Limitations section, and (c) a transparent explanation in the Response to Reviewers. The re-reviewers accepted this status as reasonable.

### 3. Response to Reviewers Uses R-A-C Format

Each response follows the Reviewer comment - Author response - Changes made structure. For accepted feedback, the response explains *what was done* and *why*. For the DELIBERATE_LIMITATION, the response explains *what was done* (14/17 paragraphs restructured), *what was not done* (3 paragraphs retained), and *why* (argumentative coherence justification).

### 4. Score Improvements Track to Specific Revisions

The quality rubrics score increased from 62.4 (Major Revision threshold) to 78.0 (upper Minor Revision). The largest gains were in Methodological Rigor (+17.7, driven by sample expansion and statistical reporting) and Evidence Sufficiency (+19.5, driven by theoretical framework integration and expanded dataset). Originality gained +9.2 primarily from the DA-CRITICAL reframing.

### 5. The Revision Tracking Template Provides Accountability

Each of the 5 items has a clear status (RESOLVED or DELIBERATE_LIMITATION), specific location of changes, and a resolution summary. The re-review process uses this tracking table as its checklist, verifying each item against the Response to Reviewers and the actual manuscript changes. This creates a verifiable audit trail.
