Excited chatter and the clattering of steel plates drown out the din of the monsoon rains: it is lunchtime in Laitsohpliah government school in the north-east Indian state of Meghalaya. The food has been cooked on-site and is free for everyone, part of India’s ambitious “midday meal” – PM Poshan – programme to incentivise school enrolment.

The scheme covers more than 1m state-run schools across the country, but the menu at Laitsohpliah is hyperlocal, thanks to a recent charity initiative in the state.

This lunchtime, apart from the staple rice and dal, there is a dish of potatoes with east Himalayan chives, cured dry fish and sohryngkham, a wild berry pickle.

Much of it has been sourced from local farmers, including parents of the pupils, while the rest has been grown in the school’s kitchen garden.

“Our students don’t like skipping meals any more,” says the headteacher, Nestar Kharmawphlang, who has taught at the school for 30 years.

Across the remote state of Meghalaya – originally part of Assam and home to the Khasi, Jaintia and Garo communities – fresh, locally sourced ingredients such as millet, fruit and wild greens are used to supplement the carbohydrate-heavy fare of rice, lentils and the occasional egg that dominate the programme’s menus.

The shift is courtesy of an initiative by the North East Society for Agroecology Support (Nesfas), which aims to make school lunches healthier, more sustainable and climate-resilient. Since 2022, Laitsohpliah has been one of 26 government-run schools in the state where lunches are transforming children’s appetites and energy levels through the use of locally grown and nutrient-rich ingredients.

Greece and OpenAI have signed a Memorandum of Understanding (MoU) to expand the integration of artificial intelligence (AI) in the country, eyeing education and SME utility.

Dubbed “OpenAI for Greece,” the collaboration between Greece and OpenAI was signed at the Hellenic Expo event, with key government officials in attendance. Prime Minister Kyriakos Mitsotakis, Onassis Foundation President Anthony Papadimitriou, and OpenAI Chief Global Affairs Officer Chris Lehane inked their signatures on the document.

Greece will pioneer the mainstream use of ChatGPT Edu, a tailor-made version of the AI chatbot for educational institutions. The MoU will back a phased pilot starting in the next academic session to integrate ChatGPT Edu into its educational system.

Under the first phase, authorities will focus on improving AI literacy among students and teachers in select institutions, with the second phase featuring a nationwide rollout. The Onassis Foundation will lead the implementation of ChatGPT Edu with The Tipping Point, which will be handling teacher onboarding.

“From Plato’s Academy to Aristotle’s Lyceum—Greece is the historical birthplace of western education,” said Lehane. “Today, with millions of Greeks using ChatGPT on a regular basis, the country is once again showing its dedication to learning and ideas.”

Going forward, a joint task force comprising representatives from the Prime Minister’s Office and the Ministry of Education will supervise the pilot project. Meanwhile, OpenAI will provide technical support and co-design a teacher’s training manual focusing on safety and productivity.

Launched in 2024, ChatGPT Edu has succeeded at leading universities, including Harvard and Oxford. OpenAI executives are confident of extended adoption levels in Europe, given its GDPR compliance, while offering students and teachers access to OpenAI’s latest models.

MoU poised to support the local startup ecosystem

Besides pushing to improve the local educational landscape, the MoU will launch the Greek AI Accelerator Program. The program will support Greek firms that are starting to build AI products and emerging technologies in partnership with Endeavor Greece.

Successful firms will have access to OpenAI technology and credits while receiving technical mentorship from OpenAI engineers. Furthermore, the program will offer tailored workshops on regulatory compliance and global safety standards while providing international exposure to local firms.

Before the MoU with OpenAI, Greece had taken early steps with AI to future-proof key industries. The country rolled out a national blueprint for AI while backing an AI-based platform to stifle the spread of fake news in Greek cyberspaces.

Indonesia unveils evaluation mechanism for responsible AI development

At the moment, Indonesia’s AI Ethics Guideline is still under development, but a self-assessment by local AI developers via an incident reporting system provided the Ministry with data for the evaluation mechanism.’

The Ministry’s Director of AI and New Technology Ecosystems, Aju Widya Sari, disclosed that the ethical guidelines will promote inclusiveness, safety, transparency, and accessibility. Following the above, the evaluation mechanism will reflect the ethical guidelines, allowing AI developers to operate under the highest global standards.

“The evaluation of the ethical guidelines will be carried out gradually to ensure ethical and responsible AI governance,” said Sari.

While not expressly stated, the evaluation mechanism will involve a multi-layer process involving rigorous checks from pre-deployment to post-deployment. Furthermore, pundits opine that the mechanism will feature metrics to measure fairness, transparency, privacy, and safety.

Sari disclosed that the evaluation mechanism will offer Indonesia the benefits of sustainability in its economy, social demographics, and environment. Indonesia has since launched an AI Center of Excellence to boost adoption demographics amid a keen stance for public safety.

Furthermore, the Southeast Asian country has made a significant play to deepen its talent pool for emerging technologies via a raft of initiatives.

AI regulation sweeps through the ecosystem

Amid the global push for AI innovation, attempts at regulation have gathered significant steam over the last year. While the EU has surged ahead with a regulatory playbook, other regions are adopting a cautious stance on AI rules for service providers.

Japan has adopted a friendly stance, while Switzerland is opting to remain neutral toward regulations, allowing the ecosystem to develop at its own pace. Meanwhile, UNESCO is riding a wave of international collaborations to promote ethical AI development, signing MoUs with Jamaica, Bangladesh, and the Netherlands.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

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The use of artificial intelligence in medical education is raising urgent questions about quality, ethics, and oversight. A new study explores how AI is being deployed in training programs and the risks of implementing poorly validated systems.

The research, titled “Artificial Intelligence in Medical Education: A Narrative Review on Implementation, Evaluation, and Methodological Challenges” and published in AI in 2025, presents a sweeping review of the ways AI is already embedded in undergraduate and postgraduate medical education. It highlights both the opportunities offered by AI-driven tutoring, simulations, diagnostics, and assessments, and the methodological and ethical shortcomings that could undermine its long-term effectiveness.

How AI is being implemented in medical education

The study found four major areas where AI is reshaping medical training: tutoring and content generation, simulation and practice, diagnostic skill-building, and competency assessment.

AI-driven tutoring is already gaining ground through large language models such as ChatGPT, which generate quizzes, exam preparation tools, and academic writing support. These tools have been shown to improve student engagement and test performance. However, the research underscores that such systems require constant human supervision to prevent factual errors and discourage students from outsourcing critical thinking.

Simulation and practice environments are another area of rapid development. Machine learning and virtual reality platforms are being deployed to train students in surgery, anesthesia, and emergency medicine. These systems deliver real-time performance feedback and can differentiate between novice and expert performance. Yet challenges persist, including scalability issues, lack of interpretability, and concerns that students may lose self-confidence if they rely too heavily on automated guidance.

Diagnostic training has also been revolutionized by AI. In specialties such as radiology, pathology, dermatology, and ultrasound, AI systems often outperform students in visual recognition tasks. While this demonstrates significant potential, the study warns that biased datasets and privacy concerns linked to biometric data collection could reinforce inequities. Over-reliance on automated diagnosis also risks weakening clinical judgment.

Competency assessment is the fourth area of innovation. Deep learning and computer vision tools now enable objective and continuous evaluation of motor, cognitive, and linguistic skills. They can identify expertise levels, track errors, and deliver adaptive feedback. Still, most of these tools suffer from limited validation, lack of generalizability across contexts, and weak clinical integration.

What risks and challenges are emerging

Enthusiasm for AI must be tempered by a recognition of its limitations, the study asserts. Methodologically, fewer than one-third of published studies rely on randomized controlled trials. Many evaluations are exploratory, small-scale, or short-term, limiting the evidence base for AI’s real impact on education.

There are also risks of passive learning. When students turn to AI systems for ready-made solutions, they may bypass the critical reasoning that medical training is designed to foster. This dynamic raises concerns about the erosion of clinical decision-making skills and the creation of over-dependent learners.

Ethical challenges are equally pressing. Training data for AI systems is often incomplete, unrepresentative, or biased, leading to disparities in how well these tools perform across different populations. Compliance with privacy frameworks such as GDPR remains inconsistent, especially when biometric or sensitive patient data is used in educational platforms. Unequal access to AI resources also risks widening the gap between well-resourced and low-resource institutions, exacerbating inequalities in global medical training.

The study also highlights gaps in faculty preparedness. Many educators lack sufficient AI literacy, leaving them unable to properly supervise or critically evaluate AI-assisted teaching. This threatens to create an uneven landscape in which some institutions adopt AI thoughtfully while others deploy it without adequate safeguards.

What must be done to ensure responsible adoption

The study provides a clear roadmap for addressing these challenges. At its core is the principle of human-in-the-loop supervision. AI should complement but never replace instructors, ensuring that students continue to develop critical reasoning alongside digital support.

The authors call for more rigorous research designs. Longitudinal, multicenter studies and randomized controlled trials are needed to generate evidence that is both reliable and generalizable. Without such studies, AI’s promise in medical education remains speculative.

Curriculum reform is another priority. AI literacy, ethics, and critical appraisal must become standard components of medical training so that students can understand not only how to use AI but also how to question and evaluate it. Educators, too, require training to guide responsible use and prevent misuse.

Finally, the study presses for inclusivity. Access to AI-driven tools must be extended to low-resource settings, ensuring that medical education worldwide benefits from innovation rather than reinforcing divides. Regulatory frameworks should also evolve to cover privacy, fairness, and accountability in AI-assisted learning.