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ACRR 2018 Alignment Statement

Last reviewed: May 2026. Updated when the platform, AI methodology, or data handling practices change materially.

This page documents GradeMap’s alignment with the Australian Code for the Responsible Conduct of Research 2018 (ACRR 2018), published by the Australian Research Council and Universities Australia.

The ACRR 2018 establishes eight principles for responsible research practice. GradeMap is an AI-assisted study coaching tool, not a research tool. However, because GradeMap supports students working on research-adjacent tasks — literature reviews, academic writing, source evaluation, and citation — alignment with the ACRR 2018 principles is directly relevant to how the platform is built and used.

Of the eight principles: 6 are fully met and 2 are partial, with known gaps documented below.

1. Honesty

Fully met

“Be honest in all aspects of research; do not fabricate, falsify, or misrepresent.”

GradeMap’s coaching is Socratic — it asks questions rather than writing for students. The referencing agent is explicitly prohibited from fabricating DOIs, dates, or publication details. AI knowledge cutoffs and potential for error are disclosed to users on every session. The tool cannot be used to generate submission-ready content without the student authoring it.

2. Rigour

Fully met

“Apply rigorous standards; challenge assumptions; interrogate results critically.”

Coaching responses are grounded in student-provided subject content — outlines, rubrics, and research sources pasted in by the student — rather than free invention from training data. The referencing agent cross-checks citations against the source material the student provides. Users are explicitly told to verify all factual claims from primary sources.

3. Transparency

Fully met

“Be transparent about methods, data sources, conflicts, and limitations.”

AI provider (Anthropic), model names, and their specific uses are publicly disclosed on this transparency page. The coaching methodology (Socratic, no ghostwriting) is documented. Training data cutoffs are disclosed. Content is not used for AI training — this commitment is stated in both the privacy policy and the transparency page. University AI policy adaptation means constraints are applied and traceable.

4. Fairness

Fully met

“Treat all people fairly; ensure data and findings are used equitably and without discrimination.”

Student data (subject content, coaching sessions, personal profile) is not used to train AI models under GradeMap’s data processing agreement with Anthropic. No student data is sold or shared with third parties beyond the AI processing pipeline. All tiers receive the same feature set — there is no paywall on integrity-critical features.

5. Respect

Fully met

“Respect research participants and the people whose data or work informs research.”

Students retain full ownership of their content. GDPR-aligned data rights are built in: students can export all their data at any time and request full deletion. Data collection is limited to what is required to provide the coaching service. Student content is transmitted to Anthropic’s API only to generate responses for that student and is not retained by Anthropic for training.

6. Recognition

Partial

“Acknowledge the contributions of others; cite sources accurately.”

The referencing sub-agent generates correctly formatted citations in APA 7th, Harvard, and Chicago styles and audits student reference lists for errors. Students are coached to cite sources rather than rely on AI paraphrasing. The coaching methodology explicitly does not produce submission-ready content that would obscure the student’s own intellectual contribution.

Known gaps: GradeMap assists students in citing correctly but cannot verify that a student actually cited all sources they used. Responsibility for accurate attribution in submitted work remains with the student. No formal authorship or contribution tracking is implemented for collaborative assessment types.

7. Accountability

Partial

“Take responsibility for research activities and outputs; maintain records.”

All coaching sessions are stored per user and can be reviewed via session export. The AI transparency page documents who is responsible for the platform and how to contact GradeMap. BYO API key users are made aware that their data processing terms differ from the standard platform agreement.

Known gaps: GradeMap does not currently produce a formal record of AI involvement that could be attached to a submitted assignment (for example, an AI use declaration artefact). Students are responsible for declaring AI use to their institution in accordance with that institution's requirements.

8. Promotion of responsible research

Fully met

“Foster a culture of responsible research practice.”

The platform is built around the principle that AI should support learning, not replace it. Coaching enforces a tutoring methodology that keeps the student as the author. Feature toggles allow students and institutions to set the level of AI involvement. The AI policy adaptation engine enforces university-specific AI use constraints during coaching. An integrity line is a first-class design principle: every interaction is designed to feel like tutoring, not outsourcing.

Tracked gaps and remediation

The two partial alignments above (Recognition and Accountability) share a common root: GradeMap assists students with academically appropriate work but cannot enforce or certify that the student complied with their institution’s specific AI declaration requirements at the point of submission.

Planned remediation (not yet implemented):

  • AI use declaration artefact — a per-session summary (mode, features used, session duration) formatted for attachment to an assignment cover sheet, where the institution requires disclosure.
  • Collaborative assessment support — group assessment types are currently treated as individual; no contribution tracking is implemented.

These gaps are tracked as future work. They do not represent a breach of the ACRR 2018 — they represent areas where the platform can provide further support to students seeking to comply with their institution’s AI use requirements.