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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grammarlyJan 29, 2026

Agentic AI vs. generative AI: What’s the Difference and When to Use Each (opens in new tab)

While generative AI focuses on creating content like text and images through prompt-based prediction, agentic AI represents a shift toward autonomous goal achievement and execution. By combining the creative output of large language models with a continuous loop of perception and action, these technologies allow users to move from simply generating drafts to managing complex, multi-step workflows. Ultimately, the two systems are most effective when used together, with one providing the ideas and the other handling the coordination and follow-through. ### Distinguishing Creative Output from Autonomous Agency * Generative AI functions as a responder that produces new content—such as text, code, or visuals—by predicting the most likely next "token" or piece of data based on a user’s prompt. * Agentic AI possesses "agency," meaning it can take a high-level goal (e.g., "prepare a client kickoff") and determine the necessary steps to achieve it with minimal guidance. * While tools like Midjourney or GitHub Copilot focus on the immediate delivery of a specific creative asset, agentic systems act as proactive partners that can use external tools, manage schedules, and make independent decisions. ### The Underlying Mechanics of Prediction and Action * Generative models rely on Large Language Models (LLMs) trained on massive datasets to identify patterns and chain together original sequences of information. * Agentic systems operate on a "perceive, plan, act, and learn" loop, where the AI gathers context from its environment, executes tasks across different applications, and adjusts its strategy based on the results. * The generative process is typically a direct path from input to output, whereas the agentic process is iterative, allowing the system to adapt to changes and feedback in real-time. ### Practical Applications in Content and Workflow Management * Generative use cases include transforming rough bullet points into polished emails, summarizing long documents into flashcards, and adjusting the tone of a message to be more professional. * Agentic use cases involve higher-level orchestration, such as monitoring document revisions, consolidating feedback from multiple stakeholders, and automatically sending follow-up reminders. * In a project management context, an agentic system can draft a project plan, identify owners for specific tasks, and update timelines as milestones are met or missed. ### Navigating Technical and Operational Limitations * Generative AI is susceptible to "hallucinations" because it prioritizes probabilistic output over factual reasoning or logic. * Agentic AI introduces complexity regarding security and permissions, as the system needs authorized access to various apps and tools to perform actions on a user's behalf. * Current agentic systems still require human oversight for critical decision-making to ensure that autonomous actions align with the user's intent and organizational standards. To maximize efficiency, you should utilize generative AI for the creative phases of a project—such as brainstorming and drafting—while delegating administrative overhead and coordination to agentic AI. As these technologies continue to converge, the focus of AI utility is shifting from the volume of content produced to the successful execution of complex, real-world results.

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