Institutional Review Processes

A major global academic publisher has recently retracted nine research papers at once by the same author. The author is a well-known senior professor in Economics and Business Administration at one of Ireland’s leading universities—widely regarded as a destination for sponsored and international postgraduate students. The papers were published in two top-tier Q1 journals, both with impact factors above 10 (≈10 and 11). All nine papers were published between 2018 and 2023. The retractions occurred at different times, but the reason was the same. Reason for retraction: The author was simultaneously the editor handling submissions for the two journals. He received his own manuscripts, assigned reviewers, and ultimately made the final publication decision on his own work. Following an investigation supported by an independent research-integrity adviser, the publisher concluded that this constituted a serious conflict of interest and proceeded with retracting all nine papers. The professor has publicly objected to the decision, arguing that there is no scientific flaw in the papers. However, the issue here is not scientific validity, but ethical integrity—specifically, the fundamental principle that no individual should act as judge over their own work. A look at the author’s Google Scholar profile shows an exceptionally high output, with an H-index exceeding 85, classifying him as a hyper-prolific author. As the saying goes: Not everything that glitters is gold. #ResearchRetraction #ResearchIntegrity #ConflictOfInterest #AcademicPublishing #Elsevier #NotEverythingThatGlittersIsGold

Edtech is often criticised for poor quality, misuse of student data and limited learning impact (I’ve voiced those concerns myself several times). But we can’t hold systems accountable without first showing what good or exceptional performance looks like. Once that’s clear, we can create competitive pressure and drive improvement.  ⬇️ Excited to finally share our paper in HSCC Springer Nature that outlines key benchmark criteria for high-quality EdTech. The paper summarises the work our research group has been doing over the past three years. It focuses on educational impact and edtech’s added value for students’ learning. 📚 After an extensive literature review and cross-sector consultations, we’ve developed a multidimensional framework grounded in the “5Es” — efficacy, effectiveness, ethics, equity, and environment.  Efficacy and Effectiveness combine experimental evidence with process-focused metrics and pedagogical implementation studies. Broader metrics focus on ethical data processing, inclusive and equitable approaches and edtech’s environmental impact. 👇 The fifteen tiered impact indicators already guide a comprehensive and flexible evaluation process of international policymakers, educators, EdTech developers and certification bodies (see EduEvidence - The International Certification of Evidence of Impact in Education and our case studies). 🙏 Huge thanks to all who contributed, especially through our participatory Delphi process. Your insights were invaluable! Nicola Pitchford Anna Lindroos Cermakova Olav Schewe Janine Campbell /Rhys Spence Jakub Labun Samuel Kembou, PhD Tal Havivi/ Ayça Atabey Dr. Yenda Prado Sofia Shengjergji, PhD Parker Van Nostrand David Dockterman Stephen Cory Robinson Andra Siibak Petra Vackova Stef Mills Michael H. Levine  #EdTech #ImpactMeasurement #5Es #EdTechQuality #EdTechStandards 👇 Read here or download from:

🇺🇦 More lessons from 4 years of Ukrainian resistance to Russian warfare : « information defence for democratic resilience » By the Digital Policy Hub & Centre for International Governance Innovation (CIGI) By Halyna Padalko, PhD 🛡️ On the defensive side, government centres, venture-backed start-ups and non-governmental organization (NGO) watchdogs run machine-learning (ML) pipelines that produce real-time alerts on coordinated inauthentic behaviour, deepfake videos and narrative shifts. ⚔️ On the offensive side, ministries employ generative media, from multilingual subtitling to synthetic spokespeople such as “Victoria Shi” to deliver rapid, values-aligned messages that galvanize support abroad and bolster morale at home, while precision deepfake “counterpunches” sow confusion in hostile audiences. 🇺🇦 Ukraine’s response is effective because it is deliberately plural: 🔹military intelligence and stratcom units plug directly into AI platforms built by start-ups such as Osavul, LetsData, Open Minds and Mantis Analytics, while investigative newsrooms Texty.org.ua and fact-checking NGOs such as VoxUkraine and Detector Media use similar tools to contextualize or debunk falsehoods. 🔹This networked architecture accelerates innovation and diffuses verification capacity across society, creating an “information shield” that denies Russia’s disinformation campaigns the “oxygen” of surprise. 🔹Rapid legislative reform (for example, Media Law 2022, Advertising Law 2023) and alignment with the EU Digital Services Act (DSA) provide legal scaffolding for transparency, user rights and platform accountability. In parallel, the Ukraine’s Ministry of Digital Transformation’s WINWIN AI Centre of Excellence is spearheading a Ukrainian-language large language model (LLM) to anchor domestic AI services and reduce dependence on foreign tech. 🎓Ukraine treats education as national security. Media literacy rates surged, driven by state programs (Filter), massive open online courses (Diia.Education) and hands-on academies (PROMPTO). 🔹Grassroots hackathons and EU-supported training translate civic awareness into professional skill sets, ensuring that technical advances are matched by a population capable of critical consumption.

Impact evaluation is a crucial tool for understanding the effectiveness of development programs, offering insights into how interventions influence their intended beneficiaries. The Handbook on Impact Evaluation: Quantitative Methods and Practices, authored by Shahidur R. Khandker, Gayatri B. Koolwal, and Hussain A. Samad, presents a comprehensive approach to designing and conducting rigorous evaluations in complex environments. With its emphasis on quantitative methods, this guide serves as a vital resource for policymakers, researchers, and practitioners striving to assess and enhance the impact of programs aimed at reducing poverty and fostering development. The handbook delves into a variety of techniques, including randomized controlled trials, propensity score matching, double-difference methods, and regression discontinuity designs, each tailored to address specific evaluation challenges. It bridges theory and practice, offering case studies and practical examples from global programs, such as conditional cash transfers in Mexico and rural electrification in Nepal. By integrating both ex-ante and ex-post evaluation methods, it equips evaluators to not only measure program outcomes but also anticipate potential impacts in diverse settings. This resource transcends technical guidance, emphasizing the strategic value of impact evaluation in informing evidence-based policy decisions and improving resource allocation. Whether for evaluating microcredit programs, infrastructure projects, or social initiatives, the methodologies outlined provide a robust framework for generating actionable insights that can drive sustainable and equitable development worldwide.

An important signal from Washington: The #US Senate Armed Services Committee has included, as part of its version of the National Defense Authorization Act #NDAA for Fiscal Year 2027, key measures aimed at strengthening both #Ukraine and Europe’s deterrence and #defense architecture: ◽ Any reduction in the US military force posture in #Europe or relinquishment of US command of the #SACEUR position would be prohibited until the impact on US and #NATO interests has been assessed and certified to Congress as being in the national interest. US Army prepositioned stocks in Europe would also be protected from removal. ◽ Before reducing permanent or rotational US troops in a NATO country, the impact on NATO’s #deterrence and defense requirements would have to be assessed. ◽ EUCOM would be required to assess whether the US and NATO can continue to meet theater campaign plans, support NATO regional plans, and fulfill NATO defense and deterrence requirements following recent changes to US force posture in Europe. ◽ #Russian grey-zone activities would have to be reported within seven days. ◽ The Secretary of Defense would be directed to engage #German counterparts on establishing a joint program for the co-development and co-production of air defense and air-to-air munitions capabilities. ◽ The Ukraine Security Assistance Initiative (USAI) would be extended through 2029, with authorized funding increased to $750 million. ◽ A new US-Ukraine Strategic Defense Innovation Working Group would focus on drone technology. ◽ NDAA funds could not be used for activities that would recognize Russian sovereignty over internationally recognized Ukrainian territory. ◽ The DoD would be directed to provide Ukraine with intelligence support for operations to defend or retake its internationally recognized territory. The bill is not yet law and now moves to the full Senate for further consideration.

You can’t have a good program without good evaluation ‼️ Over the last decade, I have designed and run a lot of programs. The most difficult and important part has always been the evaluation process I’ve learned to be wary of “perfect” feedback. When participants tell me a program was flawless and doesn’t need to change, it usually means I didn’t ask the right questions, or I didn’t create the conditions for honest reflection. It’s good for the ego and for reporting back to funders/external stakeholders, but it’s not useful for growth. Evaluation should surface what didn’t land, what could be clearer, and what needs to be rethought. It requires designing questions that go beyond “Did you enjoy this?” and instead get closer to answering “What actually changed for you?” or “What didn’t work in the way you expected?” When time and resources allow, a mixed approach tends to be the most meaningful. Surveys, short written reflections or even simple sticky note responses can capture immediate, unfiltered reactions. However, it’s often in post-program interviews where the depth really emerges. That’s where people articulate the nuances, the contradictions, and the things they wouldn’t write down. The goal isn’t just to prove that a program worked. It’s to understand how the next iteration can be meaningfully better.

Are your programs making the impact you envision or are they costing more than they give back? A few years ago, I worked with an organization grappling with a tough question: Which programs should we keep, grow, or let go? They felt stretched thin, with some initiatives thriving and others barely holding on. It was clear they needed a clearer strategy to align their programs with their long-term goals. We introduced a tool that breaks programs into four categories: Heart, Star, Stop Sign, and Money Tree each with its strategic path. -Heart: These programs deliver immense value but come with high costs. The team asked, Can we achieve the same impact with a leaner approach? They restructured staffing and reduced overhead, preserving the program's impact while cutting costs by 15%. -Star: High impact and high revenue programs that beg for investment. The team explored expanding partnerships for a standout program and saw a 30% increase in revenue within two years. -Stop Sign: Programs that drain resources without delivering results. One initiative had consistently low engagement. They gave it a six-month review period but ultimately decided to phase it out, freeing resources for more promising efforts. -Money Tree: The revenue generating champions. Here, the focus was on growth investing in marketing and improving operations to double their margin within a year. This structured approach led to more confident decision-making and, most importantly, brought them closer to their goal of sustainable success. According to a report by Bain & Company, organizations that regularly assess program performance against strategic priorities see a 40% increase in efficiency and long-term viability. Yet, many teams shy away from the hard conversations this requires. The lesson? Every program doesn’t need to stay. Evaluating them through a thoughtful lens of impact and profitability ensures you’re investing where it matters most. What’s a program in your organization that could benefit from this kind of review?

After testing 50+ AI tools, these 8 free options maintain complete academic integrity. Most academics avoid AI completely. They're terrified. But here's what they're missing: Not all AI tools violate integrity. Some actually enhance it. The difference is knowing which ones. Picture this researcher nightmare: You use ChatGPT for literature review. Submit your paper. Editor runs plagiarism detection. Flags AI-generated content. Immediate rejection. Your reputation damaged permanently. After testing every major AI research tool, I found the truth. Eight tools actually improve academic integrity. They help you find better sources. Analyze research more thoroughly. Never generate content for you. The 8 integrity-safe AI research tools: 1. Semantic Scholar - Discovers relevant research papers using AI search - Helps find sources you'd never locate manually - Shows citation context and paper influence 2. Elicit - Assists systematic literature reviews - Extracts key findings from multiple papers - Organizes research themes automatically 3. Research Rabbit - Maps citation networks visually - Reveals research connections and trends - Helps identify influential papers quickly 4. Connected Papers - Creates visual literature landscapes - Shows relationships between studies - Guides research direction discovery 5. Scite - Analyzes how papers cite each other - Distinguishes supporting vs contradicting citations - Improves research quality assessment 6. Litmaps - Visualizes research evolution over time - Tracks how ideas develop chronologically - Identifies research gaps and opportunities 7. Inciteful - Recommends papers based on your interests - Uses AI to suggest relevant literature - Personalizes research discovery process 8. Consensus - Synthesizes evidence across studies - Provides AI-powered research summaries - Helps evaluate scientific consensus The secret successful researchers know: AI can be your research accelerator. Not your content creator. Use it to find and analyze. Never to write or generate. These tools enhance human intelligence. They don't replace it. Help you work smarter. Never compromise your ethics. Your research deserves the best tools available. As long as they maintain your integrity. Which AI research tool will you try first? Save this post. Your research efficiency depends on it. Follow me for more ethical AI strategies that enhance academic work.

𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐀𝐋𝐂𝐎𝐀++ 𝐛𝐞 𝐚 𝐡𝐚𝐛𝐢𝐭, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚 𝐫𝐮𝐥𝐞? If you’re in pharma or biotech, ALCOA++ is probably second nature to you. But it’s more than just a compliance rule - it’s a way of working that keeps data reliable and trustworthy. We talk about it all the time, but what does it actually look like in real-world scenarios? Let’s look at some real-world examples. 📌 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐚𝐛𝐥𝐞 – Who did what? 🔹 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: A sample analysis is recorded, but without the analyst's initials. Later, an OOS (Out of Specification) result is found. If we don’t know who performed the test, investigating root cause becomes a nightmare! 📌 𝐋𝐞𝐠𝐢𝐛𝐥𝐞 – Can you read it? 🔹 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Handwritten temperature logs in a cleanroom are smudged and unreadable. When auditors ask for records, nobody can verify whether storage conditions were maintained. 📌 𝐂𝐨𝐧𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐧𝐞𝐨𝐮𝐬 – Recorded in real-time 🔹 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: A stability study result is noted two days later from memory. Could you recall exact values? Real-time data entry = real credibility. 📌 𝐎𝐫𝐢𝐠𝐢𝐧𝐚𝐥 – The first, unaltered record 🔹 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: A QC analyst re-enters test results in a new sheet after realizing an error. Instead of correcting the original entry with an explanation, they discard the first record. Oops! Major compliance risk. 📌 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 – No room for errors 🔹 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: An autoclave run is recorded as 121°C for 15 minutes, but the printout shows it only reached 118°C. A small slip, but a huge impact on sterility assurance. 𝐀𝐧𝐝 𝐧𝐨𝐰, 𝐭𝐡𝐞 "++" 𝐩𝐚𝐫𝐭 – 𝐰𝐡𝐞𝐫𝐞 𝐰𝐞 𝐠𝐨 𝐛𝐞𝐲𝐨𝐧𝐝 𝐀𝐋𝐂𝐎𝐀: 📌 Complete – No missing pages, no hidden data 📌 Consistent – Follows a standard, traceable format 📌 Enduring – Stored securely for the required retention period 📌 Available – Accessible for audits and investigations anytime 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐀𝐋𝐂𝐎𝐀++ 𝐦𝐚𝐭𝐭𝐞𝐫? Because in pharma, "Good enough" isn’t good enough. Patients rely on our integrity. Regulators demand transparency. It’s not just about compliance - it’s about trust. #ALCOA #Dataintegrity #Qualitymanagementsystem #Pharma #QA

Excited to share our latest work on advancing the evaluation of the Long-Term Real-World Impacts of AI-Powered tools in health care contexts published in JMIR Publications flagship journal. link to the paper —> https://lnkd.in/dzDA3syg While AI holds immense promise for transforming clinical outcomes and operational efficiency, its real-world adoption has been slower than expected. This is partly due to insufficient evaluation frameworks that take critical factors like clinical integration, economic sustainability, and real-world impact into account. Current evaluation methods fall short in several critical ways: ❌ Outdated frameworks that don’t reflect the dynamic, complex nature of AI in clinical environments ❌ Implementation gaps, with little guidance on integrating AI tools seamlessly into healthcare workflows ❌ Overemphasis on technical metrics like sensitivity and specificity, overlooking real-world effectiveness ❌ Lack of standardized methods that extend beyond controlled trials to real-world settings ❌ Insufficient validation of existing guidelines, with many frameworks missing transparent methodologies or stakeholder input ❌ Regulatory blind spots, one example is a recent study that showed that nearly 50% of FDA-approved AI devices lack clinical validation data To address these gaps, we developed AI for IMPACTS, a comprehensive framework that evaluates AI tools beyond technical performance and the confines of clinical studies, focusing on long-term, real-world outcomes. It’s structured around seven key dimensions: I: Integration, Interoperability & Workflow M: Monitoring, Governance & Accountability P: Performance & Quality Metrics A: Acceptability, Trust & Training C: Cost & Economic Evaluation T: Technological Safety & Transparency S: Scalability & Impact This framework is designed to guide AI’s successful adoption in diverse healthcare settings while ensuring safety, sustainability, and clinical value in real-world contexts. It lays the groundwork for further validation through expert consensus and testing of the framework in real-world health care settings. It is important to emphasise that multidisciplinary expertise is essential for assessment, yet many assessors lack the necessary training. In addition, traditional evaluation methods struggle to keep pace with AI’s rapid development. To ensure successful AI integration, flexible, fast-tracked assessment processes and proper assessor training are needed to maintain rigorous standards while adapting to AI’s dynamic evolution. A huge thank you to my brilliant co-authors Noe Brasier, Emanuele Laurenzi, Prof. Dr. Sabina Heuss, Stavroula-Georgia Mougiakakou, Arzu Çöltekin, and Prof Dr Marc K Peter for their outstanding contributions. 🙌 #AIinHealthcare #DigitalHealth #HealthTech #ArtificialIntelligence #HealthcareInnovation #AIEvaluation #HealthCareTransformation #RealWorldImpact #IMPACTSFramework #InterdisciplinaryResearch