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After the havoc AI has caused in classrooms—think student cheating and data mishandling—lawmakers have stepped in to take the reins. New protocols and frameworks are now setting the terms for how technology is applied to curb fraud and keep assessments fair.
In the US, new rules such as FERPA 201 shape how educators use AI. In the EU, vendors face stricter checks on safety and human review. Schools are tightening privacy policies around AI data. Assessments are shifting toward tasks that test critical judgment. Credentials are moving to common digital standards. Proctoring is adapting to better support diverse learners.
So what exactly has been building in EdTech? We have all heard about debates between AI positivists and negativists online, but it seems to be a natural cycle of life, much like the case with the web. What is important is that federal grant dollars are now tied to responsible AI use. At the same time, states are strengthening student data privacy rules. On the tech front, issuers are aligning digital credential formats to common standards, and learners with special needs are recognized with updated accommodations. These changes directly affect how educators, assessors, trainers, and hirers will operate going forward. Join us as we explore the implications of these initiatives and spell out the action steps for Q4 2025.
If your AI tools do not protect privacy, are not transparent, or cut humans out of decision-making, you could lose access to federal education funds.
In July 2025, the US Department of Education issued a Dear Colleague letter that turned AI guidance into a funding blueprint. It lays out where federal dollars can go, pointing to AI-based instructional materials, tutoring and diagnostic tools, and career advising systems. A new grant priority supports projects that meet these rules, with four conditions at the core: educators stay in charge, tools are accessible, systems are transparent, and data privacy is protected.
Reviewing your digital learning environment, testing platforms, and related tools for compliance with DOE rules on privacy, transparency, and human oversight will determine your grant eligibility.
The DOE’s principles can become your policy benchmark. If your programs depend on federal funds or follow US education rules, you may need to update your guidelines to match.
Brussels is on a similar wavelength. The EU AI Act, in force since August 2024, puts education-focused AI in its high-risk category. This covers systems for admissions, grading, and detecting cheating. Beyond showing auditors what their systems do, institutions must be prepared to prove that humans still control the outcomes.
Our hard line in proctoring prohibits unsolicited data grabs or running assessments on autopilot, whether the mode is AI-enabled, live, or fully automated. For partner institutions and certifying bodies, this means your proctoring setup is already speaking the regulators’ language.
AI in classrooms is under sharper scrutiny, with data handling now a top concern.
Weak spots are piling up, exposing murky training data practices, reliance on consumer AI tools, and fresh breaches. The Student Privacy Policy Office (SPPO) is raising the bar through its FERPA 201 and Transparency webinars, telling schools to clarify how AI processes student data.
States are stepping up, too. The National Conference of State Legislatures (NCSL) 2025 tracker shows dozens of bills that would expand privacy rules for edtech vendors and give people more power to take legal action when data is mishandled.
If you run AI-driven LMS, proctoring, or assessment tools, be ready to demonstrate exactly how student data is collected, stored, and used. Expect to update privacy notices and vendor contracts.
SPPO’s FERPA guidance gives you a clear federal starting point. You can use it to shape member policies and also prepare for state rules that may require more detailed notices.
Enterprise AI tools now run under stricter contracts. These often state that student data cannot be used for training, must be exportable or deletable upon request, and require vendors to show they have a working incident-response plan.
Vendors face closer checks on how they use and store training data. Candidates are starting to receive plain-language privacy notices, and providers are expected to prove they can delete or control data when asked.
In the UK, Digital Futures for Children has introduced a voluntary EdTech Code of Practice and certification in 2024, focusing on child data protection and rights-based design. Defend Digital Me says the UK government has asked the ICO to draft a statutory Code of Practice for children’s data in education in 2025, but it must cover the full data lifecycle across schools and edtech—not just products. They flag unresolved DfE audit gaps and risks from the Data Use and Access Bill, urging future-proof, rights-based guidance.
For schools and vendors, it sets a higher bar for handling data in AI tools. That means collecting less personal information and making accountability clear.
Data privacy isn’t a box we tick. Institutions can see what we collect, how long we keep it, and when we delete it. That transparency already meets US district requirements and keeps us ready if UK buyers use the proposed Code of Practice as their benchmark.
The surge in generative AI use is testing long-held assumptions about how to protect academic integrity.
Case in point: 26% of US teens now use ChatGPT for schoolwork, and 65% of college students use a gen AI chatbot each week. In a recent survey by the American Association of Colleges and Universities and Elon University, 59% of educators said cheating has risen since these tools became widespread. AI plagiarism detectors have been the go-to remedy. But many colleges worry AI detectors cause more harm than good with fake positives, especially when it comes to ESLs. Attention is now shifting from playing catch-up with cheating to assessments designed to hold up even when students have access to AI:
To guide redesign, planning frameworks are circulating. One example is the AI-resilience or cognitive-demand grid, a matrix that maps which tasks are most open to AI shortcuts and which demand genuine reasoning. At the same time, guidance on when assessors may responsibly use AI for marking and feedback has been available since 2023 for US, UK, and AU.
Assessments are now expected to highlight the real markers of academic mastery, such as reasoning, explanation, and applied knowledge.
Currently, the spotlight is on higher education, with other assessment providers likely to follow suit.
Extended essays, projects, and oral tests are reclaiming space because they reveal the depth of learning that dashboards and generic badges can’t replicate. Our job is to ensure proctoring keeps the assessment credible and on pace, giving staff control over when AI monitoring is on without adding friction for anyone involved.
Digital credentials now have a common rulebook.
In May 2025, the World Wide Web Consortium (W3C) approved Verifiable Credentials 2.0, a global standard for secure and verifiable digital records of learning and skills. Education and HR standards bodies—1EdTech and HR Open Standards—are aligning their frameworks to support it, so schools, training providers, and employers can use the same “language” when exchanging credentials. Major players are already making the shift. Anthology, a higher ed platform for learning management, has begun supporting Open Badges 3.0, the badge format built to work with VC 2.0.
With W3C setting Verifiable Credentials 2.0 as the standard and vendors shifting to Open Badges 3.0, machine-verifiable formats are becoming the default. If your systems still issue older-style records, they may soon be harder to recognize across platforms.
The 1EdTech and HR Open partnership is designed to remove friction between education-issued credentials and hiring systems. That means certifications will need to be packaged in ways that employers’ platforms can read and verify without custom work.
As more vendors add Open Badges 3.0, internal upskilling programs can issue credentials that outside systems can check directly. This makes employee records easier to validate, but only if your platform keeps pace with updates. Adoption will depend on vendor roadmaps, so timing matters.
On the hiring side, alignment between HR and education standards promises cleaner verification of candidate credentials. It is uncertain how quickly applicant-tracking systems will integrate these standards. Expect a mixed environment where some records verify automatically and others require manual checks for the time being.
Check your systems. See if your learning or HR platforms can already issue, store, or verify VC 2.0 and Open Badges 3.0. If not, note when vendors plan to add it.
Update your records. Ensure your badges or credentials include the new standards details: issuer name, metadata, and verification link.
Follow cross-use updates. Keep an eye on the 1EdTech and HR Open guidance so the credentials you issue or accept can move smoothly between education systems and employer platforms without custom integrations or manual data reformatting.
Our work with certifying bodies shows how much effort still goes into checking the authenticity and portability of assessment results. The adoption of new standards matters because it makes exam outcomes easier to validate and trust across platforms.
Neurodivergent candidates necessitate the sector to rethink how proctoring cues are applied.
Proctoring systems that track signals like “looking away” or “talking” can misinterpret normal behavior in neurodivergent test-takers. This creates false positives that don’t reflect misconduct. To address this, sector frameworks such as the Personal Needs Profile (PNP) and Access for All provide ways to carry accommodations throughout the whole exam process. These include bypassing gaze checks, using alternative verification like a room scan plus activity log, or adding a human review step when a case is disputed.
Mistaken readings add reviews, appeals, and stress. Proctoring settings should let gaze checks be skipped when accommodations apply, with a clear backup method in place.
High-stakes exams lose credibility if neurodivergent candidates are misread. Policies should spell out how accommodations affect signals and appeals. RFPs often ask vendors to show non-gaze options tied to frameworks like PNP/Access for All.
Errors in compliance tests damage trust. A short matrix of signals, limits, and alternatives helps clarify risks. Candidate notices reduce disputes by explaining which behaviors may trigger alerts and how to request changes.
In hiring, a false positive can turn candidates away. Offering a non-gaze option, such as activity checks plus human review, protects both fairness and integrity.
To fix problems with gaze signal detection, we’ve added controls that let staff turn off gaze and face tracking. This makes it easier to adapt the system when accommodations are needed. Assistive tools like screen readers, speech-to-text, and extra time or breaks add flexibility. This gives staff and candidates confidence in the process.
By Q4 2025, privacy, new credential standards, AI-resilient formats, and accessible proctoring have moved from talk to action. Early adopters will keep their funding, credibility, and candidates’ trust. Those on the fence risk audits, penalties, and lost relevance.
Are you ready to set the pace?
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