Scaffolded Grant Proposal Using peerScholar

Melanie Jeffrey teaches environmental and Indigenous health courses in Human Biology and Public Health. She is a settler of English, Welsh, and Scottish descent from Parry Sound, on the traditional territory of the Anishinaabeg in the Williams Treaties area. She has been working with Indigenous pedagogies since 2008, when she began to collaboratively develop a first Indigenous science breadth requirement with staff, students, faculty and community members. Her research and teaching interests include Indigenous environmental justice, the health of peoples and lands, and the nexus between Indigenous Knowledge Systems and STEM, including health, ecology, and engineering approaches to address community-directed research agendas. In teaching and research, her role is to listen, connect people, and bridge ways of knowing to inform social justice and equity in health and environmental problem solving.

Melanie Jeffrey

Sessional Instructor, Human Biology, Faculty of Arts & Science; Assistant Professor Non-tenure Stream, Indigenous Health, Dalla Lana School of Public Health

Objectives

As part of the course, Global Health: Indigenous Health (HMB 433H1F, Fall 2025)), this scaffolded grant proposal assignment is designed to help students develop authentic research and writing skills in the context of Indigenous health. Students learn to formulate inquiry-based research questions grounded in community-defined priorities and integrate Indigenous methodologies and ethical principles. A key AI-resistant feature is the emphasis on critical source discernment: students must locate and synthesize at least ten credible sources beyond course materials (peer-reviewed articles, grey literature, and Indigenous community documents), which requires genuine reading and judgment rather than automated text generation.

Through the Create → Assess → Revise structure, students engage in iterative writing, peer feedback, and metacognitive reflection, making their learning process visible. PeerScholar’s design fosters peer learning, as students both give and receive constructive feedback, helping them refine their proposals and justify decisions about accepting or rejecting suggestions. By focusing on positionality, ethical engagement, and evidence-based reasoning, the assignment creates deliberate barriers to AI shortcuts and ensures students demonstrate authentic understanding and process – not just a polished product.

Process

Students engage with the assignment through a multi-step process:

Step 1: Students draft their proposal in peerScholar (Create Phase – 10%)

Students write a 2,000-word draft directly in peerScholar, including all required sections (title page, positionality statement, executive summary, background, community engagement, methodology, outcomes, timeline, references).
Why this limits AI use: Writing in-platform and integrating Indigenous priorities, ethics, and OCAP® principles requires authentic reasoning and source discernment. Students must use at least 10 credible sources (peer-reviewed, grey literature, Indigenous documents), which invites real reading and synthesis, as opposed to generic AI text.

Step 2: Students provide peer feedback (Assess Phase – 10%)

Students review five peers’ proposals and give constructive feedback on clarity, community alignment, and integration of Indigenous methodologies.
Why this limits AI use: Feedback must be context-specific and values-driven, fostering genuine peer learning and collaborative judgment rather than generic, automated responses.

Step 3: Students revise and reflect (Revise Phase – 10%)

Students revise their proposal based on peer feedback and submit the final version.
Students complete a reflection on their learning process, explaining how feedback shaped their revisions or why certain suggestions were not incorporated.
Why this limits AI use: Reflection requires metacognitive engagement and evidence of decision-making, ensuring students demonstrate their learning process rather than relying on AI-generated text.

Future-Focused Skill Development

This activity aligns with the University of Calgary’s STRIVE model for designing assessments that thoughtfully incorporate or resist generative AI. It emphasizes Transparency by clearly outlining expectations for each phase – Create, Assess, and Revise- and requiring students to document their revisions and reflect on their learning process.

In addition, it promotes Responsibility by engaging students in ethical, community-driven research design that respects Indigenous priorities and methodologies, reinforcing accountability in scholarly work. Finally, it advances Equity by offering an inclusive structure that supports diverse learners through scaffolding, peer feedback, and an iterative approach, while allowing space for students to articulate their positionality and values. Together, these elements create an assessment that is authentic, reflective, and resistant to AI shortcuts.

Student Feedback

Students frequently exhibit initial reluctance to engage with unfamiliar pedagogical approaches, particularly during their final year of study. The mock grant proposal assignment, however, has proven to be intellectually stimulating and professionally advantageous. It introduces students to a specialized genre of writing that is both transferable and marketable, while simultaneously fostering a collaborative learning environment. This exercise promotes creativity, personal development, and refinement of written communication skills. Structured abstract workshops enable students to articulate and sharpen their ideas through peer dialogue in a formative, non-evaluative context.

Furthermore, the assessment’s grading schema incentivizes constructive peer interaction and cultivates the capacity to receive and integrate feedback, an indispensable competency in academic and professional spheres. By emphasizing originality and critical engagement, the assignment explicitly discourages reliance on generative artificial intelligence, thereby reinforcing the value of authentic scholarly practice.

Course Syllabus Statement on GenAI Use

In her Indigenous health course, Professor Jeffrey includes a statement on the prohibition of AI use in assignment in the syllabus:

“While AI tools can assist with research, writing, and organization, they have significant limitations in the context of Indigenous Studies. AI systems are trained on large datasets that often reflect colonial biases, incomplete knowledge, and mainstream narratives, which may misrepresent or exclude Indigenous perspectives, languages, and worldviews.

Students are reminded that:

AI cannot accurately reflect the lived experiences, cultural protocols, or spiritual dimensions of Indigenous communities.
AI-generated content should never be used as a substitute for engaging with Indigenous voices, scholarly sources, or community-based knowledge.
Misuse of AI in this context may perpetuate harmful stereotypes or inaccuracies.

You are encouraged to approach Indigenous Studies with respect, critical thinking, and a commitment to ethical research practices.”

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