Curriculum Development Challenges

𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐚𝐧𝐝 𝐩𝐚𝐭𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐨𝐟𝐭𝐞𝐧 𝐩𝐮𝐫𝐬𝐮𝐞𝐝 𝐰𝐢𝐭𝐡 𝐥𝐢𝐭𝐭𝐥𝐞 𝐫𝐞𝐠𝐚𝐫𝐝 𝐟𝐨𝐫 𝐚𝐜𝐭𝐮𝐚𝐥 𝐦𝐚𝐫𝐤𝐞𝐭 𝐮𝐭𝐢𝐥𝐢𝐭𝐲, 𝐚𝐢𝐦𝐞𝐝 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐚𝐭 𝐜𝐚𝐫𝐞𝐞𝐫 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭, 𝐚𝐰𝐚𝐫𝐝𝐬, 𝐨𝐫 𝐦𝐞𝐫𝐞 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧. I have been noticing, 𝐝𝐞𝐬𝐩𝐢𝐭𝐞 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐧𝐮𝐦𝐛𝐞𝐫𝐬—𝐦𝐢𝐥𝐥𝐢𝐨𝐧𝐬 𝐨𝐟 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬, 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐩𝐚𝐭𝐞𝐧𝐭𝐬, 𝐚𝐧𝐝 𝐚 𝐬𝐭𝐞𝐚𝐝𝐲 𝐬𝐭𝐫𝐞𝐚𝐦 𝐨𝐟 𝐏𝐡𝐃𝐬—𝐭𝐡𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐜 𝐨𝐮𝐭𝐩𝐮𝐭 𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐚𝐧𝐝 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐝𝐢𝐬𝐩𝐫𝐨𝐩𝐨𝐫𝐭𝐢𝐨𝐧𝐚𝐭𝐞𝐥𝐲 𝐥𝐨𝐰. According to a UNESCO report, while over two million research papers were published globally in 2022, less than 30% had any cited industrial or societal application. Similarly, in the U.S., an analysis by the National Bureau of Economic Research (NBER) reveals that only about 5% of patents filed by universities reach the commercialization stage. An increasing trend among academicians involves co-founding companies as a superficial indicator of market engagement. Registration alone is relatively inexpensive in many countries, and without substantial follow-through—market traction, talent acquisition, funding, or a public footprint—these entities may remain on paper only. A recent investigation revealed that several award-winning researchers, who claimed numerous corporate collaborations and company foundations, had little to no market visibility, casting doubt on the actual impact of these ventures. Many academics, unfortunately misuse resources. 𝐈𝐧𝐩𝐮𝐭𝐬: 📌 Academia needs to prioritize real, industry-defined challenges rather than theoretical, manufactured problems. Direct engagement with industry experts, SMEs, and MSMEs can provide authentic, field-tested insights that are foundational for impactful research. Industry problems are keep changing thus academicians, startup fraternities have to update regularly through thoroughly ground level research, market survey and industry trends.📌 From problem identification to prototyping and commercialization, industry partners should be deeply involved. 𝐌𝐚𝐤𝐢𝐧𝐠 𝐟𝐢𝐞𝐥𝐝 𝐯𝐢𝐬𝐢𝐭𝐬 𝐚𝐧𝐝 𝐫𝐞𝐠𝐮𝐥𝐚𝐫 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐜𝐨𝐦𝐩𝐮𝐥𝐬𝐨𝐫𝐲 𝐟𝐨𝐫 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐜 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫𝐬 𝐜𝐚𝐧 𝐟𝐨𝐬𝐭𝐞𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐨𝐟 𝐠𝐫𝐨𝐮𝐧𝐝-𝐥𝐞𝐯𝐞𝐥 𝐢𝐬𝐬𝐮𝐞𝐬. 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬: 📌 Kalasalingam’s engineering faculty and students have partnered with small-scale industries to create low-cost solar and biomass energy solutions. 📌Chitkara University’s agribusiness program collaborates with small-scale food producers to enhance food storage and packaging solutions. 📌BAMU has collaborated with local agricultural industries and farmers to develop cost-effective soil health monitoring systems. #industryproblems #msme #patent

Stop the nonsense: A call to adopt a radically boring approach to genAI technologies in higher education #EdTech2026 🔥🔥 🔥 James Brunton "Journal articles, institutional/national/international guidance documents, and LinkedIn posts alike call on staff in higher education to gain, and/or improve their ‘AI literacies’. The discourse around how to deal with the emergence of this glut of innovative, disruptive, and rapidly evolving technologies centre around inevitability, that the technology is here, students are already using it, and staff should, or must, accept and engage with it. Discourse frequently acknowledges the ethical issues accompanying AI tools, while in the same breath saying their use is unavoidable and desirable. Non-engagement is now framed as an extreme, irresponsible position to take; responsibility, in exploring and establishing ways of integrating AI technologies into teaching and learning falls to each individual as part of their professional responsibilities; limited systemic accountability, in that the role of the institution is limited to generating policy and high level guidance, providing the typically limited degree of training and educational development in teaching and learning, and setting expectations for individual staff to develop their own capacity. As was the case before this particular ed-pocolyse, e.g. with the pivot to remote teaching during COVID, the discourse on AI in higher education is frequently divorced from discussions of existing institutional digital competency frameworks and related, strategic, resourced capacity building for staff and students. Most of the ideas and proposed interventions in this space are doomed to fail as they are constructed on the shaky foundations of existing, dysfunctional dynamics in higher education. This is to say that higher education staff typically do not need to have a particular level of training or expertise in teaching and learning to be hired, and then do not have to attain any particular level when in their role, with their professional development left as a voluntary, individual endeavour. The literature is clear on higher education staff's overwhelming workloads, and accompanying levels of stress and burnout, and who therefore have little time for professional development, even when so motivated. Career progression pathways typically do not motivate staff to engage in time-consuming professional development in the teaching and learning domain; e.g. academic promotion processes typically motivate staff to put more attention on research. This presentation proposes an alternative, radically boring approach for how higher education staff can be positioned to be able to approach any technology, including new, innovative, and disruptive technologies, in their teaching and learning work. This approach is grounded in existing models of staff capacity building and a rejection of the nonsensical idea that higher education staff capacity is magically infinite."

Here is a Rose, Thorn and Bud summary of today's release of the Designing for Education with Artificial Intelligence: An Essential Guide for Developers Rose (Positive aspects): - The guide provides comprehensive recommendations for developers to create responsible AI-enabled educational technologies, focusing on five key areas: designing for education, providing evidence, advancing equity, ensuring safety, and promoting transparency. - It emphasizes shared responsibility between developers and educators in building trust and addressing risks associated with AI in education. - The report encourages developers to align their products with educational values, visions, and evidence-based practices. - It highlights the potential of AI to improve accessibility, inclusion, and support for diverse learner populations. - The guide promotes transparency and open communication between developers and the educational community. Thorn (Challenges or negative aspects): - Developers face complex challenges in addressing algorithmic bias, protecting civil rights, and ensuring equity in AI-enabled educational products. - There are significant risks associated with AI in education, including privacy concerns, data security issues, and potential for harmful content or misinformation. - The rapidly evolving nature of AI technology makes it difficult for developers to stay current with best practices and regulatory requirements. - Balancing innovation with responsibility may be challenging for developers, especially when considering the "race-to-release" pressures in the tech industry. - Achieving true transparency in AI systems, particularly with "black box" technologies like large language models, remains a significant challenge. Bud (Opportunities for growth): - There's potential for AI to revolutionize personalized learning, adaptivity, and support for diverse learner needs. - The guide encourages developers to engage in co-design processes with educators, students, and other stakeholders, which could lead to more effective and trusted educational technologies. - The emphasis on evidence-based development and evaluation presents an opportunity for more rigorous and impactful educational technology. - The call for developers to contribute to AI literacy in the broader edtech ecosystem could lead to more informed and empowered users of educational technology. - The promotion of a "dual stack" approach, balancing innovation and responsibility, presents an opportunity for developers to create more holistic and ethical development processes. Check out the full report ⬇

76 per cent of teachers now use AI. Here is what the evidence says about what that actually means. Four data sources published in the first months of 2026 tell a consistent story about AI and schools in England. The National Education Union surveyed 9,408 members in February 2026. 76% now use AI tools in their day-to-day work. A year ago that figure was 53%. The share using no AI tools at all has fallen from 47% to 24% in 12 months. That rate of adoption is striking. And what sits alongside it even more so. Half of those schools have no AI policy whatsoever, for staff or students. Two thirds have no policy specifically for students. The NEU asked the same question a year ago. The figures have barely changed. The Royal Society commissioned TeacherTapp to ask 9,250 teachers about AI literacy in March 2026. Only 14% described themselves as confident across the practical, technical, and human dimensions of AI. 34% reported limited confidence in most aspects. 37% said AI literacy is not currently addressed in their teaching at all. Only 2% said it is covered across most subjects. So teachers are using the tools, without training, without policy, and largely without teaching young people anything about them. The Royal Society's rapid review of AI literacy frameworks makes clear that AI literacy is being left almost entirely to computing teachers. The capabilities it actually requires, including evidence evaluation, critical scepticism, understanding of model limitations, and ethical reasoning, belong equally across all areas of the curriculum, from science to citizenship. The TeacherTapp data shows where the support gaps fall hardest. 23% of classroom teachers report no school support of any kind for developing AI literacy. Headteachers are far more likely to report that informal guidance is available (45%) than classroom teachers (28%). Structured professional development reaches only 18% of teachers overall. The consequences show up in the NEU data. 66% of secondary teachers report that pupils' critical thinking has declined as a result of AI use. In primary schools the figure is 28%. The finding that should give us pause: younger teachers, those most familiar with the tools, are more likely to report this (57%) than those aged 50 and over (39%). What teachers want is practical and immediate. What they are receiving, where they are receiving anything at all, is informal and inconsistent. The tools are in classrooms. The question is whether anything is going to be built around them to ensure young people understand what the tools are, what they are doing, and what they cannot do. What I am listening to: 'Running Up That Hill' by Kate Bush What I am reading: 'Hamnet' by Maggie O'Farrell See you in the kitchen. Prof Rose Luckin UCL and EVR Ltd (Article Sources available on request) #AIinEducation #AILiteracy #TeacherTraining #EdTech #CriticalThinking #EducationPolicy #SkinnyOnAIED #RoyalSociety #NEU #SchoolsPolicy

* Building Relationships: Take the time to get to know students individually. Learn about their interests, hobbies, and what motivates them. For example, a teacher might start the year with a survey asking students about their favorite things or spend a few minutes each day chatting with individual students about their lives outside of school. * Showing Empathy and Understanding: Recognize that students' behavior is often a reflection of their experiences and challenges. Be patient and understanding, and try to see things from their perspective. For example, if a student is consistently late to class, a teacher might ask them privately if everything is okay at home rather than immediately punishing them. * Creating a Safe and Supportive Classroom: Establish a classroom environment where students feel safe to take risks, make mistakes, and express themselves. This can be achieved through clear expectations, consistent routines, and a focus on positive reinforcement. For example, a teacher might create a classroom agreement with students outlining expectations for behavior and communication. * Providing Opportunities for Success: Offer students opportunities to shine and experience success, regardless of their academic abilities. This can be achieved through differentiated instruction, flexible grouping, and a focus on individual growth. For example, a teacher might allow students to choose their own projects or assignments based on their interests and strengths. * Celebrating Diversity: Create a classroom environment where diversity is celebrated and all students feel valued and respected. This can be achieved through inclusive curriculum, culturally responsive teaching practices, and opportunities for students to share their unique perspectives. For example, a teacher might incorporate diverse texts and perspectives into their lessons or invite guest speakers from different cultural backgrounds. * Using Positive Language and Reinforcement: Focus on praising effort and progress rather than just achievement. Use positive language to encourage students and build their confidence. For example, instead of saying "That's wrong," a teacher might say "That's a good start, let's try it this way." * Being a Role Model: Model the behaviors and attitudes you want to see in your students. Be respectful, compassionate, and enthusiastic about learning. For example, a teacher might share their own struggles and successes with students to show them that it's okay to make mistakes and that learning is a lifelong process.

The book "Generative AI in Higher Education: The ChatGPT Effect" examines the profound shift in the academic landscape following the rise of Large Language Models, framing the future as a period of significant educational uncertainty regarding assessment, pedagogy, and the very definition of learning. Uncertainty in Assessment and Academic Integrity A primary concern is the potential collapse of traditional methods used to evaluate student knowledge. -The "Cheating" Wildcard: There is deep uncertainty about how to distinguish between genuine student effort and AI-generated output, leading to a crisis of trust in high-stakes testing. -Obsolescence of Traditional Tasks: Standard assignments, such as the five-paragraph essay, face an uncertain future as AI can produce them in seconds, forcing educators to reconsider what "evidence of learning" looks like. -Detection Efficacy: The report highlights the unpredictable reliability of AI-detection tools, creating a volatile environment where false positives and negatives disrupt the teacher-student relationship. Pedagogical and Curricular Uncertainty The document explores the "unknown" future of how subjects should be taught when AI can serve as a universal tutor. -The Role of the Educator: There is uncertainty regarding the future role of professors—transitioning from "knowledge providers" to "learning facilitators"—and whether institutions can adapt their training fast enough. -Curriculum Lag: A critical uncertainty is the "lag" between the rapid advancement of AI capabilities and the slow pace of institutional curriculum reform, potentially leaving graduates ill-prepared for an AI-integrated workforce. .Standardized Learning Risks: There is a concern that over-reliance on AI-generated content might lead to a "homogenization" of thought, where students lose the ability to engage in unique, critical inquiry. Ethical and Socio-Economic Uncertainty The broader societal implications of AI in education introduce significant strategic wildcards. -The "AI Divide": There is profound uncertainty regarding whether generative AI will democratize education by providing personalized support or exacerbate existing inequalities between those with and without access to premium AI tools. -Data and Bias: The future reliability of AI as an educational resource is shadowed by uncertainty regarding the "black box" nature of its training data and the potential for embedded algorithmic biases to influence student worldviews. In conclusion, the document suggests that higher education is at a pivotal crossroads. The future is defined not by the certainty of AI’s dominance, but by the uncertainty of whether human institutions can reinvent themselves fast enough to harness AI's potential while protecting the core values of critical thinking and academic rigor.

Life Lessons: Building Confident Practical Skills We Wish Were Taught in School  “By failing to prepare, you are preparing to fail” Benjamin Franklin. As an educator with over 20 years of experience working in the UK, Turkey, and Saudi Arabia, I've witnessed firsthand the limitations of a purely academic-focused education. We spend years meticulously preparing students for higher education, cramming their schedules with advanced mathematics and complex scientific theories. Yet, many graduates find themselves ill-equipped to navigate the realities of adult life. This article delves into the glaring gap between academic knowledge and the practical skills essential for navigating the modern world. The Kitchen as a Classroom: Reimagining Home Economics Remember Home Economics? Once a staple in many schools, this subject, often relegated to girls, provided invaluable life skills like cooking, sewing, and basic household management. In today's world, these skills are not merely domestic; they are fundamental for independent living. Cooking nutritious meals is not just a chore; it's a cornerstone of health and well-being. Budgeting, financial planning, and understanding basic financial concepts are crucial for navigating the complexities of the modern economy. Beyond the Classroom: Navigating the Real World The modern world demands more than just academic prowess. Practical skills are essential for navigating everyday life. Learning to read a map, understand basic mechanics, and appreciate the importance of environmental sustainability are crucial for responsible citizenship. Moreover, fostering empathy and emotional intelligence are paramount. A Call to Action Redefining Education for the 21st Century It's time to re-evaluate our educational priorities. This includes: 1. Integrating life skills into the core curriculum Incorporating practical skills like cooking, budgeting, basic mechanics, and environmental awareness into the core curriculum, ensuring all students have access to this valuable knowledge. 2. Fostering creativity and critical thinking Emphasizing creative problem-solving, critical thinking, and innovation to prepare students for a rapidly evolving world. 3. Cultivating emotional intelligence: Integrating social-emotional learning into the curriculum to foster empathy, communication, and self-awareness. By embracing a more holistic approach to education, we can empower students to become well-rounded individuals, equipped with the knowledge and skills they need to navigate the challenges of the 21st century and build fulfilling lives. I encourage you to share your thoughts and perspectives in the comments section below. Let's continue this important conversation and work together to create a more meaningful and effective educational system for all. Disclaimer: This article presents a perspective on the importance of life skills in education. It is not intended to diminish the importance of traditional academic subjects.

A very important report by Reform Scotland today, on both the importance and vulnerability of Computing Science Education at school level. Any tech economy is a function of the supply of talent into it. Reform Scotland's research: 66 schools have no CS provision, that's over 32,500 pupils. 25 schools with >1000 pupils have only one CS teacher. CS teacher numbers down 25% since 2008 against a stable school role, and falling recruitment. It should no longer be a point of debate as to whether this amounts to a crisis for Scotland's tech sector of the near future. This decline is fully reversible, if our education authorities and local authorities act together now, with renewed urgency and a sense of ownership of the problem. I discussed these issues with Parliament's Education Committee this morning, link in the comments section. https://lnkd.in/esSAeUrg

Across India, almost every college has started new programmes in Computer Science, AI, Data Science, AIML and Cybersecurity. Students and parents want only these courses and demand is increasing every year. But there is a big problem. There are not enough qualified faculty for these programmes. Most students who study computer science related degrees are choosing IT jobs. Very few go for higher education and teaching. Because of this, colleges are struggling to fill faculty positions. This has become a national issue. At the same time, many Mechanical, Civil and other core engineering departments have very low admissions. Some departments are closing, and many faculty in these branches are underutilised. India cannot ignore this situation any longer. We need a simple and practical solution that can work for the whole country. The solution is to allow faculty from other engineering branches to become eligible to teach Computer Science subjects after structured training. All India Council for Technical Education (AICTE) and University Grants Commission (UGC) can approve a clear pathway. For example: Offer a one year PG Diploma or a six month Diploma in Computer Science, AI and Data Science. Conduct the programme through the NPTEL or SWAYAM platform. Include a final exam to test knowledge and skills. Once faculty complete the diploma and pass the exam, they should be officially recognised as Computer Science faculty by both AICTE and the respective universities. This will help in two ways. Colleges will get the required number of faculty for computer science programmes. Faculty from Mechanical, Civil, EEE and other branches will get an opportunity to move into computer science if they are interested and capable. Today every engineering discipline is using AI, Data Science and automation in some form. So cross training is practical and necessary. If we do not act now, the faculty shortage will only grow and many institutions will face challenges during affiliation and inspections. The government and higher education authorities need to bring a policy for this crisis soon. A simple national pathway can solve a major problem and help colleges maintain quality education in all computer science related programmes. Narendra Modi Dharmendra Pradhan Ministry of Education, Government of India, New Delhi #EngineeringEducation #HigherEducationIndia #AICTE #UGC #ComputerScience #FacultyShortage #EngineeringColleges #AI #DataScience #AIML #EducationReform #SWAYAM #NPTEL #IndiaFutureSkills #PolicyRecommendation

Some of the "worst practices" when integrating digital technologies in education that are good to remember (and avoid): 1. Technology for Technology's Sake: Simply adding technology to a lesson without a clear pedagogical purpose is ineffective. Using technology just because it's available, without aligning it to learning objectives or improving student outcomes, is a waste of resources and time. The technology should serve a clear educational goal. 2. Insufficient Teacher Training and Support: Expecting teachers to effectively integrate technology without adequate training and ongoing support is unrealistic. Teachers need time, resources, and mentorship to learn how to use technology effectively and integrate it into their teaching. "Throwing technology at the problem" without proper support leads to frustration and ultimately, ineffective use. 3. Ignoring Digital Equity and Access: Not considering the digital divide and ensuring equitable access to technology and internet connectivity for all students is a major pitfall. Some students may lack the resources to participate fully, creating an inequitable learning environment. 4. Over-reliance on Passive Learning Activities: Using technology primarily for passive activities, such as watching videos or completing online worksheets, does not leverage technology's potential for active learning, collaboration, and critical thinking. Interactive simulations, collaborative projects, and student-created digital content are far more effective. 5. Neglecting Assessment and Feedback: Using technology for instruction but failing to adapt assessment methods to leverage technology's capabilities is a missed opportunity. Technology can enable more frequent, personalized, and effective feedback, but this requires careful planning and integration into the assessment strategy. 6. Ignoring Digital Citizenship and Safety: Failing to address digital citizenship, online safety, and responsible technology use is irresponsible. Teachers need to equip students with the skills to navigate the digital world safely and ethically. 7. One-Size-Fits-All Approach: Assuming that a single technology or approach will work for all students and all subjects is ineffective. The best technology choices depend on the learning objectives, student needs, and subject matter. A flexible and adaptable approach is essential. 8. Lack of Planning and Integration: Simply adding technology to existing lessons without careful planning and integration into the overall curriculum is unlikely to be effective. Technology integration requires thoughtful planning, alignment with learning objectives, and assessment strategies.