Turning student data into an academic integrity strategy

In the era of generative artificial intelligence, upholding academic integrity requires visibility into student work and writing. This is only possible through the strategic use of student data, which lets educators evaluate not just what was submitted, but how it originated, and equips education leaders to align academic policies with the realities of AI-integrated learning.

Relying solely on the final product overlooks critical insights that the writing journey and drafting process can reveal. Student data offers a window into student effort, authorship, and engagement, turning formerly invisible behaviours into actionable intelligence.

For education leaders, student data generated during the writing process is an untapped asset; one poised to safeguard authentic student development and academic standards, while helping students develop ethical, accountable behaviors fit for the modern world.

Here’s what you need to know.

Academic integrity challenges in the AI era

Student use of AI to complete assignments has jumped from 53% in 2024 to 88% in 2025. An institution’s response to this surge isn’t simply a matter of managing potential AI misuse and protecting institutional credibility, but strengthening students’ depth of learning and the measures to accurately evaluate it. In an age where cognitive tasks can be outsourced with unprecedented ease, the very foundations of learning are at risk without mechanisms to ‘see’ how it unfolds.

Written assignments aren’t just a test of student knowledge and composition skills. The writing process is proven to improve comprehension, content learning, and critical thinking. These skills are essential for genuine student growth, academic success, graduate employability, and professional aptitude.

When AI replaces this effort, students dilute the value of their education, their qualifications, and public trust in the education system.

Jisc reports that students and learners are still unclear on what use of AI is permitted, yet increasing numbers are integrating it into their learning practices—presenting an important opportunity for institutions to train and model responsible use that offsets to fear of an overreliance on AI reducing their critical thinking, creativity, and communication skills.

The impact of AI on academic integrity strategy

Against this backdrop, academic integrity strategies must evolve. With AI-generated content becoming more frequent and harder to detect, it demands a systemic, institution-wide response that goes beyond misconduct detection to upholding authentic learning at scale.

This shift requires institutional leaders to address a series of interconnected challenges that span pedagogy, strategy, and student experience.

Pedagogic challenges

1. Adapting teaching and assessment to prioritise authentic learning and preserve credibility.
2. Scaling pedagogical practices to meet rising class sizes while maintaining quality.
3. Doing the above without adding to faculty workload, burnout, and retention issues.

Strategic challenges

1. Reducing the costs of misconduct investigations, which now average £95,000 a year in the UK.
2. Improving compliance with regulatory standards and graduate quality benchmarks.
3. Maintaining institutional reputation and stakeholder trust.

Student-centric challenges

1. Protecting student-teacher relationships, enhancing satisfaction, and reducing attrition.
2. Supporting students to develop critical thinking skills demanded by employers.
3. Developing student AI literacy, within clear boundaries that are ‘urgently needed’ .

Experts increasingly agree that meeting these challenges requires a shift from reactive detection and punishment to proactive prevention and support. This means moving away from ineffective blanket bans on AI and creating an environment that supports original work from the outset.

Student data as the foundation of academic integrity

Student data is a strong foundation of effective academic integrity policies. By using insights into how work was created, institutions can promote authentic learning and increase educator impact, all without adding to the administrative burden felt by faculty.

The approach is built on three key pillars.

1. Visibility into the writing process

Educators need insights and student data to understand how a piece of work was created. This includes information on drafting patterns, revisions, and the time spent writing. Seeing the process, not just the final product, makes it easier to spot authentic work, encourage student effort, and correct potentially damaging missteps.

2. Proactive support and intervention

Student writing data can help identify early signs that a student may be at risk of learning shortcuts or misconduct. Behaviours such as low composition time or pasted content, for example, may suggest students are struggling or disengaged from the task. Data insights in a collaborative environment allow educators to step in with timely, targeted support to guide students towards authentic work, based on evidence that validates intuition.

3. Ethical AI integration

50% of students report that they don’t know how to get the most benefit out of AI in their studies (Turnitin & Vanson Bourne, 2025). They should be supported to integrate AI into their learning practices in ways that are ethical, approved, and acknowledged. When used properly, AI can help students build skills, rather than undermine them. For example, using instant AI writing feedback to refine their argument and structure before submission.

So what exactly is ‘visibility into the writing process’ and how can integrity-conscious institutions use student data to unlock its benefits?

Student data for assessing originality: an evolution

Visibility into the student writing process has advanced in parallel with composition technology, from handwritten submissions to digital composition.

1. In the pre-digital era, limited access to external content and the use of handwritten assignments meant plagiarism was less visible, and originality harder to verify. Detection relied on educator intuition or manual comparison.
2. In the early digital era, the shift to word processing introduced more opportunities for misconduct via digital means, but also useful metadata. Submission data, such as author information and timestamps, could help verify the origin of submitted files, but gave limited insight into how the work was produced.
3. In the AI era, Turnitin Clarity provides a far more detailed view of the student writing process. By capturing composition timelines, revision history, and similarity checks, educators can build a clearer picture of whether a work has been thoughtfully developed and authentically authored—measured against the academic integrity and AI use policies set by the institution or course.

These advances offer new opportunities to support originality and academic integrity, as well as bolster student outcomes and institutional reputation.

What does visibility into the writing process look like today?

Today, visibility into the writing process is found in Turnitin Clarity, which brings newfound transparency and integrity insights to education. It provides educators with previously unavailable student writing data and insights into the composition process. This supports more informed judgments about originality, effort, and student learning.

Turnitin Clarity delivers this invaluable visibility through its:

1. Writing composition space where students can confidently navigate the writing process, integrated with their institution’s LMS.
2. Writing flags and reporting that capture student data throughout the writing process for educator review, including composition time, instances of pasted content, and revision and version history playback.
3. All-in-one integrity insights, working alongside Similarity checking and AI writing detection* to help determine the originality of written work, flag student missteps, and enable constructive, trust-building conversations with learners.
   *available with Turnitin Originality add-on.
4. Optional AI assistant, offering integrated AI assistance trained to guide students rather than simply provide answers, and open dialogue between educators and students about its role in learning.

With these insights, educators can intervene early and support students in difficulty. Plus, in cases where investigation is required, an audit trail provides an evidence base to reduce the time, cost, and ambiguity typically involved in these processes. Education leaders who prioritise such technology position their institutions at the forefront of academic integrity innovation, protecting reputation and enhancing student success.

Watch this 2-minute video to see key features in action.

The benefits of better visibility into the student writing process

Better visibility into the writing process and a preventative approach to integrity breaches benefits strategic leaders, educators, and students alike.

For education leaders

1. Reduced reputational risk associated with traditional or AI-enabled misconduct due to a culture of academic integrity that withstands technology’s ongoing evolution.
2. Reduced costs associated with investigating and proving misconduct, due to data-backed audit trails.
3. Improved compliance with accreditation standards, thanks to a lower likelihood of misconduct and higher graduate quality.
4. Enhanced student outcomes, thanks to real time insights into student engagement and progress that inform teaching efficacy.

For educators

1. A more supported, informed student-teacher relationship based on trust, not suspicion (research finds positive student-teacher relationships can deter misconduct).
2. Ability to prioritize time for feedback, rather than policing misconduct (HEPI found each misconduct investigation takes 56 minutes of academic and 106 minutes of admin time).
3. Improved educator impact and learner outcomes, as actionable insights help tutors scaffold student learning in harmony with AI.
4. Higher educator efficiency and lower burnout, thanks to accelerated integrity insights.

For students

1. Clear expectations and a safe environment to foster ethical writing practices, plus peace of mind that there is data to substantiate the originality of their work.
2. Timely, targeted feedback (proven to be one of the most effective ways to enhance student achievement).
3. Core skill development through original writing, including critical thinking and problem solving.
4. Personal skill development, such as accountability, self-regulation, and resilience.

Overview: Why should you turn student data into an academic integrity strategy?

Student writing data is more than just a by-product of their process. It is evidence of, and scaffolding for, their learning journey.

When staff and students are equipped with the right tools and policies, institutions can shift from reactive enforcement to proactive support and prevention, preserving integrity while supporting students to higher levels of attainment.

By investing in tools that illuminate the writing process, education leaders better align assessment methods with strategic goals:

1. Improving student outcomes and strengthening academic standards.
2. Ensuring fair and consistent assessment of genuine student progress.
3. Reducing the reputational risk and hidden costs of misconduct, like staff burnout.
4. Equipping students with 21st-century skills skills to thrive in education and the workforce.