grammarlyAug 13, 2026

Grammarly Authorship Is Now Available in Blackboard (opens in new tab)

Grammarly Authorship aims to make student writing transparent as AI becomes common in education. It tracks whether text was typed by a student, generated by AI, copied, or rephrased, allowing students to demonstrate their process and instructors to evaluate work with more confidence. Its new Blackboard integration brings these reports directly into assignment workflows, building on integrations with Google Docs, Microsoft Word, Word Online, Grammarly Docs, and Canvas. ## The Purpose of Grammarly Authorship - Authorship addresses concerns shared by instructors and students: - Instructors need confidence that submitted work is authentic. - Students need credit for their own contributions and protection from false accusations. - It emphasizes transparency and attribution rather than relying solely on AI detection. - Students retain control over viewing and sharing their writing-process data, while reports cannot be altered before submission. - The system recognizes: - Human-typed text - AI-generated content pasted into a document - AI-generated content created within a document - Copied text - Text rewritten with Grammarly’s generative AI ## Adoption and Reported Results - Authorship launched in Google Docs beta in October 2024 and later expanded to Grammarly Docs, Microsoft Word, and Word Online. - Students have created more than 5 million Authorship reports. - Rowan-Cabarrus Community College reported a reduction in academic-integrity violations from 27 to 1 semester-over-semester after adopting Authorship across its English department. ## Blackboard Integration Workflow - Instructors enable **Enable Grammarly Authorship** in the Originality Report section when creating a Blackboard assignment. - Students continue writing in their preferred tools and activate Authorship tracking. - Authorship automatically records the sources and origins of text. - Students generate a shareable report link and set its access to **Anyone with the link**. - They submit the link alongside their assignment through Blackboard’s normal submission process. - Instructors receive a class-level overview and can inspect an individual student’s full writing-process replay when necessary. - This allows instructors to focus attention on unusual cases instead of manually investigating every submission. ## Benefits for Students, Instructors, and Institutions - **Students** - Can demonstrate their writing process with minimal additional effort. - Receive recognition for their own thinking, whether or not they used AI. - Build responsible AI-literacy and source-attribution habits. - **Instructors** - Can require Authorship reports at the assignment level. - Review reports from a centralized Blackboard view. - Spend less time investigating and more time using writing-process evidence for instruction. - **Institutions** - Gain a scalable academic-integrity approach across departments. - Use existing writing and learning-management tools rather than requiring major workflow changes. - Establish a consistent institutional response to AI use. Grammarly Authorship’s Blackboard integration is available to Grammarly for Education customers with institution-wide plans that use Blackboard. It offers a practical way to make AI-era writing more accountable by combining student consent, process evidence, and existing assignment workflows.

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grammarlyMay 29, 2026

A University of Florida Professor Stopped Fighting AI in His Classroom: A Peer-Reviewed Study Followed (opens in new tab)

Dr. Brian Harfe addressed generative AI in student writing by redesigning an essay assignment instead of relying on surveillance or AI detectors. In a 310-essay study, students began with AI-generated drafts and revised them into essays reflecting their own views, with word-level provenance tracked through Grammarly Authorship. The results suggest that assignment design can encourage meaningful engagement and provide more reliable insight into AI use than surveys or detection scores. ## Limits of Surveys and AI Detectors - Surveys are influenced by students’ perceptions of acceptable behavior, fear of penalties, and difficulty recalling how much AI assistance they used. - AI detectors provide probabilistic judgments rather than proof. - Most detectors assess an entire document and cannot identify which passages were AI-generated or how the text developed. - These methods measure the final product, not the writing process. ## An Assignment Built Around AI - In the University of Florida course “Can We Design Better Humans? Should We?”, students had to start with a fully AI-generated essay. - They then revised it to express their own views on human cloning and genetic engineering. - Students could keep, modify, or discard as much of the AI draft as they wanted. - Because AI use was explicitly permitted, the assignment removed the incentive to conceal it. - Grammarly Authorship tracked whether each word was typed by the student, copied from AI, or drawn from another source. - Students submitted authorship reports, allowing Harfe to replay the evolution from AI draft to final essay. ## Findings from 310 Essays - The study included students from seven colleges and more than 100 majors. - Students who wrote more original text generally spent more time completing the assignment, linking time-on-task with deeper revision. - STEM students produced more human-generated text than non-STEM students, although both groups were similarly likely to agree with the AI draft. - Higher-performing students revised AI-generated material more extensively across all disciplines. - Students retained approximately 76% of the AI draft on average. - The roughly 5% who disagreed with the AI’s position revised substantially more, adding more of their own writing. - Only two students submitted the AI draft without edits, despite being explicitly allowed to do so for full credit. ## Implications for Education - Harfe’s exact assignment may not apply to every course, but its underlying principle is broadly useful: incorporate AI into learning activities rather than treating it solely as a threat. - Provenance tools provide a record of writing activity instead of an uncertain verdict about authorship. - The findings challenge the assumption that students will automatically surrender their thinking to AI when given permission to use it. - Students’ willingness to revise appears connected to academic engagement and performance. - Reflective assignments can help students evaluate AI’s strengths, weaknesses, and appropriate future uses. Instructors and institutions should focus less on detecting AI after the fact and more on designing assignments that require students to evaluate, revise, and take responsibility for AI-assisted work.

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grammarlyApr 13, 2026

The Trust Question: How Higher Education Is Really Navigating AI (opens in new tab)

Higher education’s AI challenge is fundamentally a question of trust, not simply technology adoption or resistance. Based on interviews with educators and administrators, institutions are balancing competing views about innovation, evidence, ethics, and practical outcomes. The central conclusion is that credible AI governance must make these differences visible and build shared understanding rather than rely on blanket rules or binary narratives. ## AI Policy Is a Campus-Wide Negotiation - Four recurring orientations shape institutional responses: - **Innovators** favor responsible adoption before reactive governance becomes necessary. - **Strategists** want stronger evidence before committing to change. - **Resisters** prioritize ethics, academic integrity, and institutional reputation. - **Pragmatists** focus on student success, equity, and workable implementation. - These perspectives often coexist within the same institution. - Differences between administrators, writing center leaders, and faculty can create productive debate or direct conflict. - Recognizing these mindsets helps institutions engage stakeholders more effectively. ## Institutions Need Alignment, Not More Tools - Leaders consistently asked for alignment with institutional priorities, constraints, and values—not additional technology. - Effective partners should help institutions understand trade-offs rather than impose preselected solutions. - Skeptics need language that allows them to raise concerns constructively. - Advocates for adoption must recognize that resistance often reflects responsibility rather than fear of change. - AI is forcing institutions to clarify long-standing tensions such as: - Speed versus rigor - Access versus control - Innovation versus stability ## Academic Integrity as a Trust Problem - The common starting question—how to prevent students from misusing AI—is too narrow. - Academic integrity also asks whether institutions trust students and whether students trust their institutions. - Excessive restrictions can communicate distrust, while a lack of governance can appear negligent. - K–12 and higher education face different accountability structures, but both must create guidelines that reflect their actual educational values. - Many educators are shifting: - From detection to judgment - From surveillance to discernment - From punishment to responsibility - Integrity policies therefore communicate what an institution believes learning is for. ## Governing Under Uncertainty - Leaders are tired of portraying AI as either an existential threat or a universal solution. - They need principled language for discussing uncertainty with students, faculty, families, and governing boards. - Every AI decision sends a message about institutional values and credibility. - Maintaining trust requires thoughtful governance, shared understanding, and honest engagement with uncertainty—not stricter rules alone. Institutions should treat AI governance as an ongoing process of alignment and trust-building. Rather than beginning with enforcement or technology procurement, they should clarify their values, acknowledge competing perspectives, and develop policies that support informed judgment and shared responsibility.

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