Assessment Policy Changes

Yesterday, the California Privacy Protection Agency released proposed revisions to its current rulemaking package on ADMT, risk assessments, and cybersecurity audits for discussion at an April 4th board meeting. My initial read of the proposed revisions is that they are fairly minor, with the most operationally significant changes probably being: 1️⃣ Removing requirements that appeared intended to expand deletion rights to any personal information, rather than information collected directly from a consumer. 2️⃣ Removing certain affirmative requirements to inform consumers of their ability to lodge complaints with the Agency and Attorney General. 3️⃣ Tweaks to cybersecurity audit reports, including removing the requirement to inform the Agency about any cybersecurity incident reports a business has submitted to other states or countries. 4️⃣ Tweaks to risk assessment requirements, including removing the requirement to identify a business' steps to "maintain the quality of personal information" processed by ADMT or AI. 5️⃣ Where there is a material change to data processing, a risk assessment must be updated within 45 days, no longer "immediately." In the Agency's explanation for changes, many revisions are explained as being intended to "simplify implementation at this time," so if past is prologue, we may see the original requirements return in future rulemaking packages. While the Agency's proposed revisions are fairly minor, be sure to check out Item 6, which tees up a board discussion on more substantive revisions including significantly narrowing the definitions of "ADMT" and "significant decision" as well as potentially walking back requirements tied to "behavioral advertising", "public profiling" and training ADMT/AI. Links in comments.

I have never seen such drastic changes in university education as what has happened during the past two years because of generative AI technologies. Especially student assessment is now a completely different activity than what it used to be. I am starting to think that this requires a complete paradigm change in student assessments. We should not merely measure individual student capabilities but start evaluating student-AI teams and the result of the collaboration between AIs and students. Traditional university assessments are designed to measure individual student knowledge, skills, and critical thinking. Exams, essays, and projects typically emphasize personal effort and originality, aiming to cultivate independent thinkers. While this model has worked well for centuries, it now feels increasingly disconnected from the realities of the digital age. AI tools like ChatGPT, DALL-E, and others can produce sophisticated outputs, ranging from code and essays to data analysis and creative designs. Denying students access to these tools in assessments not only misrepresents their future work environments but also hinders their ability to develop critical skills for the AI-integrated workplace. The workplace of tomorrow will not reward individuals who can outperform AI but those who can work with AI to achieve exceptional outcomes. Universities must therefore adapt assessments to evaluate how well students integrate AI tools into their workflow to address complex, real-world problems, how critically they evaluate AI outputs for accuracy and bias, and how creatively and effectively they use AI to enhance their projects and generate novel solutions. Furthermore, students’ understanding of ethical considerations, including data privacy, transparency, and responsible innovation, must also become a focal point of assessment. Transitioning to a model that evaluates collaboration between students and AI requires innovative approaches. Assignments could explicitly require AI assistance, such as asking marketing students to develop campaigns with the help of AI tools, assess their viability, and justify their strategic decisions. Grading systems might prioritize the process over the final product, evaluating how students choose and use AI tools, iterate based on feedback, and address errors in AI-generated outputs. Open-book exams could allow AI use, with students evaluated on their ability to interpret, critique, and expand upon AI-generated content. Simulated workplace scenarios, where students work as part of a team with AI, could also become a powerful tool to measure real-world readiness. However, this transition is not without its challenges. See the comment section for more. Have you already started to assess the results of student-AI collaboration or do you still consider the individual capabilities of students as the main thing to assess in university education? #AI #education #assessment #grading #capabilities

Rethinking Assessment in the Age of AI As AI reshapes the landscape of learning. It's time for a radical rethink. First, let's address the elephant in the room: the concept of 'cheating' with AI is obsolete. In an AI-saturated world, unrestricted AI use isn't cheating—it's a fundamental skill. Our goal shouldn't be to limit AI use, but to foster its ethical and effective application. With this in mind, I propose a set of principles designed to update how we evaluate learning in an AI-augmented world: ⭐ Transparency: Embrace unrestricted AI use. Require students to document and critically reflect on their AI interactions. ⭐ Process Focus: Shift emphasis from final outputs to the journey of inquiry, problem-solving, and critical evaluation. ⭐ Real-world Alignment: Design assessments that mirror complex, multifaceted challenges students will face in their future careers. ⭐ Fluidity: Allow for dynamic, non-linear assessment methods that adapt to individual learning paths and AI engagement levels. ⭐ Integration: Embed assessment seamlessly into the learning process, moving away from high-stakes, isolated testing events. These principles aren't just tweaks to the existing system; they represent a fundamental shift in how we conceptualise learning and assessment. They acknowledge that in an AI-saturated world, the ability to regurgitate information is far less valuable than the capacity to critically engage with, ethically apply, and innovatively expand upon AI-generated insights. Critics might argue this approach is too permissive. But I contend that any attempt to restrict AI use is not only futile but counterproductive. Our focus should be on fostering responsible, creative, and ethical AI use – not trying to police its application. The real challenge for educators isn't preventing AI use; it's designing learning experiences that make simplistic AI reliance pointless. When our assessments truly measure higher-order thinking, ethical reasoning, and creative problem-solving, AI becomes a powerful tool for learning, not a crutch to avoid it. This is not just about keeping pace with technology; it's about fundamentally reimagining education for a world where human-AI collaboration is ubiquitous. It's time to stop playing catch-up and start leading the charge. #FutureOfEducation #AIinLearning #AuthenticAssessment #EducationalInnovation

When are privacy assessments needed, and what must they cover? Here's what state laws require.   Many privacy programs conduct privacy assessments of business activities. These can include privacy threshold assessments, privacy impact assessments (PIAs), and data protection impact assessments (DPIAs). Privacy teams may use the processes for different purposes, including to identify privacy requirements and risks, support data inventories and records of processing, or to document assessments when required by law.    In the U.S., state privacy laws require a specific type of assessment when certain triggers are met. These triggers can include certain data processing activities, like "sales" of personal data, uses for targeted advertising, or certain profiling or automated processing. Some personal data types, like sensitive personal data or personal data relating to minors, can also trigger assessment requirements. When required by state privacy laws, assessments generally need to be conducted and documented before the personal data processing starts--meaning before the product or feature goes live, advertising campaign launches, or vendor use starts. They also must be updated before practices change.   State laws can require assessments to cover specific elements that must be documented in the assessment. There are some common elements that most states require, and there are unique ones that only specific states require.    For companies focused on privacy compliance, it's important to conduct assessments when required, and to make sure the assessments address all required elements. Documented assessments often need to be provided to state regulators on request, and companies subject to California's CCPA will ultimately need to make certifications to the state that assessments were conducted when required in 2026 (i.e., before in-scope personal data processing occurred).   Attached are summaries of when assessments are required, and what they must cover, under state comprehensive privacy laws. These include new assessment content requirements that Connecticut will require starting next month for certain profiling activities, and that Delaware's recently amended privacy law will require starting next year.    If your organization hasn't reviewed and updated its privacy assessment processes in recent years, it's time for a review. Consider:   1️⃣ Validating policies and business processes require assessments before in-scope business activities launch 2️⃣ Refreshing training and guidance for business stakeholders on assessment processes 3️⃣ Tailoring assessment processes and platforms used so assessments are conducted (and not screened out) when state law triggers are met 4️⃣ Confirming all required questions and considerations are covered in the assessment 5️⃣ Sanitizing business stakeholder responses so documented assessments are ready for regulator review, and 6️⃣ Reviewing and reassessing when activities change and when required by law

𝗙𝗼𝗿 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀, 𝘄𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗞𝗣𝗜𝘀. Now, this is only half of the job done. Erasmus+ Youth applications 𝗶𝗻 𝟮𝟬𝟮𝟲 𝘄𝗶𝗹𝗹 𝗹𝗼𝗼𝗸 𝘃𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁. The policy roadmap is already here. If you want to predict the future of your project, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗿𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝗶𝗻𝗲 𝗽𝗿𝗶𝗻𝘁 of today's policy frameworks. It tells. 𝗙𝗼𝗿𝗴𝗲𝘁 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗞𝗣𝗜𝘀! The shift from "𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝗵𝗲𝗮𝗱𝘀" to "p𝗿𝗼𝘃𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝗰𝗵𝗮𝗻𝗴𝗲" is explicitly documented in three key resources: 𝟭 𝗘𝗨 𝗬𝗼𝘂𝘁𝗵 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (𝟮𝟬𝟭𝟵–𝟮𝟬𝟮𝟳): Explicitly demands evidence-based policy making and "participatory evaluation" methods. 𝟮 𝗥𝗔𝗬 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 (𝗥𝗔𝗬-𝗠𝗢𝗡/𝗟𝗧𝗘): The EU’s data backbone now prioritises verified impact over simple satisfaction scores. 𝟯 𝗖𝗼𝘂𝗻𝗰𝗶𝗹 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝗬𝗼𝘂𝘁𝗵 (𝟮𝟬𝟮𝟮–𝟮𝟬𝟮𝟳): Calls for systemic activity evaluation that links local project results to European policy goals. 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗯𝗿𝗶𝗻𝗴𝗶𝗻𝗴? We are seeing a move away from purely quantitative KPIs. The "tick-box" era of evaluation is ending. The Commission is signalling a 𝗻𝗲𝗲𝗱 𝗳𝗼𝗿 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲-𝗯𝗮𝘀𝗲𝗱 𝗶𝗺𝗽𝗮𝗰𝘁 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴—they want to know the "story" of the change, not just the number of participants. Reread it! We will need to 𝘁𝗲𝗹𝗹 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱, not the number of people! 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲? To align with this shift for 2026, we need to upgrade our evaluation toolkits now. A hybrid model is emerging as the new gold standard: • 𝗠𝗼𝘀𝘁 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲 (𝗠𝗦𝗖) 𝗠𝗼𝗱𝗲𝗹: To capture the qualitative, human stories of empowerment. • 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗛𝗮𝗿𝘃𝗲𝘀𝘁𝗶𝗻𝗴: To map those specific stories directly to the EU Youth Goals. • If you can prove the l͟i͟n͟k͟ ͟b͟e͟t͟w͟e͟e͟n͟ ͟a͟ ͟p͟a͟r͟t͟i͟c͟i͟p͟a͟n͟t͟'͟s͟ ͟p͟e͟r͟s͟o͟n͟a͟l͟ ͟s͟t͟o͟r͟y͟ ͟a͟n͟d͟ ͟a͟ ͟m͟a͟j͟o͟r͟ ͟E͟U͟ ͟p͟o͟l͟i͟c͟y͟ ͟o͟b͟j͟e͟c͟t͟i͟v͟e͟,͟ your proposal becomes incredibly difficult to reject. Are you preparing your evaluation frameworks for this shift?

I would like to share a second AI in Ed SoTL article entitled, “Redesigning Assessment for the Generative AI Era: A Framework for Educators” by Khlaif, et al. (2025) (https://lnkd.in/eAeV6BxJ ). Khlaif and colleagues offer a timely and practical rethinking of assessment practices grounded in educational integrity, learner agency, and AI fluency. Their work proposes a multidimensional framework designed to ensure that assessment continues to reflect meaningful learning even when AI is involved at every stage. The authors argue that generative AI has fundamentally disrupted assessment by: - Making traditional recall tasks obsolete - Complicating academic integrity enforcement - Blurring lines between student work and AI contribution - Expanding students’ access to instant feedback and explanations Rather than focusing on catching misuse, Khlaif et al. advocate for: - Authentic, process-driven assessments - Metacognitive reflection on tool use - Evaluation of student + AI co-production - Assessment of higher-order thinking, not output alone Four Key Dimensions 1) Pedagogical Dimension. Assessment must align with active learning, inquiry, critical thinking, and student-centered design. 2) Ethical Dimension. Includes transparency, academic honesty, consent, bias awareness, and AI literacy. 3) Technological Dimension. Focuses on tool selection, AI capability analysis, and appropriate use boundaries. 4) Assessment Dimension. Calls for redesigned methods including: - performance-based tasks - iterative submissions - reflective writing - multimodal evidence - collaborative problem-solving - AI-augmented portfolios Educators are urged to: - Require students to document how they used AI - Compare drafts with and without AI assistance - Integrate oral defense, peer review, and process documentation - Blend human judgment with AI-supported analytics - Incentivize learning, not just product creation Rather than equating AI use with cheating, the authors propose a new definition: Integrity means honestly representing the relationship between human and AI contributions. This shift reframes assessment in terms of transparency, reflection, and ethical agency. Khlaif et al. make a compelling case that assessment, not content, is where AI will make the biggest impact on learning systems. If assessment fails to evolve: - learning outcomes become artificial - grades become meaningless - student agency weakens - equity gaps worsen If redesigned with AI in mind: - creativity expands - students build meta-AI literacy - authentic learning becomes visible - assessment becomes more human, not less Reference Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174.

From 1 January 2026, Royal Institution of Chartered Surveyors RICS has introduced a cap on APC assessment attempts and many candidates are misunderstanding what this means. You now have four attempts to pass your APC. There is no automatic fifth attempt. A further final attempt may only be considered after completing additional requirements (typically 12 months of further structured experience), and is subject to approval. From 1 January 2026, all assessment attempts count towards your total regardless of outcome or pathway. Attempts made before this date do not count. Everyone starts from a clean slate. For Candidates: Do not submit to maintain momentum. Submit because you are ready. Four attempts go faster than you think — sessions, preparation time, and life events all add up. Treat every submission as if it were your only one. If you are referred, read the report properly. Address every point. Resubmitting with minor edits is no longer a low-risk option — it now carries real consequences. Know your gaps before your counsellor does. Map your competencies. Keep your records up to date. Do not cram at the end. For Counsellors: Your sign-off carries more weight than ever. Approving a candidate who is not ready uses one of their four attempts. Have honest conversations early — that is how you properly support your candidate. For Assessors and Employers: Write referral reports that give candidates something to act on. Put candidates forward when they are genuinely ready, not when it is convenient. The standard has not changed. But the margin for error has. Four attempts. Use them wisely. #RICSAPC #QuantitySurveying #Chartership #MRICS #APC #ProfessionalDevelopment #QS #ConstructionCareers #RICS #Construction #CareerDevelopment #Leadership

The NSW Government scrapped the controversial proposal that would have forced workers to take bullying or harassment claims to court before seeking compensation. Here’s what’s changing: 👉🏻 A new 8-week assessment process will replace the court requirement. 👉🏻 Excessive work demands are now recognised as a cause of psychological injury. 👉🏻 Workers with <30% impairment can now settle for a lump sum earlier. 👉🏻 The threshold for weekly payments for life is being eased in over time. 👉🏻 Vicarious trauma is now clearly compensable. 👉🏻 WHS powers for prevention are being strengthened. 👉🏻 Legal cost reforms will ensure lawyers act in the best interest of injured workers. Plus, a $344M workplace mental health package has been announced - including 50 new SafeWork inspectors focused on psychological injuries and immediate wraparound support for affected workers. What will these changes mean for your workplace?