UAE: Robot teachers enhance learning by 8% over human teachers in primary school, study finds
Conducted by researchers at the Rochester Institute of Technology (RIT) in Dubai, the study revealed the positive effects of AI and robotics integration at the elementary school level.
The project involved the use of a personalised, AI-powered robot that engaged directly with students, leading to an average academic performance improvement of eight per cent compared to traditional human-led teaching.
Led by Dr Jinane Mounsef, chair of the university's Electrical Engineering and Computing Sciences Department, the research highlighted the potential of personalisation in robotic tutoring in a real-world educational setting.
The robot, known as Duet, employed powerful machine learning algorithms and the ROS (Robot Operating System) framework to predict a student's proficiency level with 100 per cent accuracy through indicators such as test scores, task completion time, and emotional engagement. It was then able to adjust the challenges and learning materials dynamically to suit each student's needs.
Dr Mounsef, said, 'Through post-diagnostic exams we found that the experimental group of students using the AI-robot system showed a significant improvement rate over the control group. This demonstrates that such systems can provide a powerful tool to improve efficiency and augment education outcomes.'
She added, 'We aim to take our work in cognitive development forward to explore the use of robotics in emotional intelligence. This will involve deploying a human-looking robot that can interact with students on a daily basis to ask questions, gather data and ultimately make recommendations that can help to address their concerns.'
Experts urge balanced implementation
Education experts in the UAE have welcomed the findings, while also calling for thoughtful and balanced implementation in early learning environments.
'Yes — cautiously, and contextually,' said Shifa Yusuffali, CEO and Founder of IdeaCrate and MENALAC Board Member. 'The evidence is growing. Studies like the one conducted by the University of Cambridge in 2022 have shown that programmable robots can support skills like collaboration and sequencing among children as young as four, particularly in guided group settings.
"Similarly, research from MIT Media Lab and Tufts University (Bers, 2018) has shown that screen-free robotics, like KIBO, can introduce young learners to computational thinking without displacing the sensory, social, and creative elements of early education.'
However, Yusuffali emphasised that such technology should support, not replace, the essence of childhood learning.
'Technology in early years should never lead — it should follow. It should follow the child's innate sense of wonder, their need to move, to ask, to connect. In the right setting, with thoughtful facilitation, AI and robotics can become interesting companions in that journey — but never the driver.
'Children need connection more than they need content. They need to play before they can program. And they need to be seen — not just as future learners or digital natives, but as whole human beings with their own pace, questions, and stories.'
Interactive storytelling devices
The country's largest preschool chain has also already begun integrating AI tools in classrooms to support learning goals in developmentally appropriate ways, said, Dr Vandana Gandhi, CEO and founder of British Orchard Nursery and Teacher Training Centre.
'We advocate innovation that supports developmental needs through technology. Our EYFS curriculum ensures that all children have access to the latest age-appropriate technology tools and methodology for a seamless transition to big schools. Educators use AI-driven tools and AI assistants for teaching, assessment, curriculum planning, and monitoring progress,' said Gandhi.
She noted that nurseries are also introducing tech numeracy games, interactive storytelling devices, and smart learning stations across their branches.
'These tools have already shown a positive impact on attention span and cognitive engagement — while preserving the nursery's commitment to a play-based, sensory-rich environment,' added Gandhi.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Economy ME
18 minutes ago
- Economy ME
AlphaGenome: How will Google DeepMind's AI model transform our understanding of the human genome?
Google DeepMind unveiled AlphaGenome, a groundbreaking artificial intelligence (AI) model poised to transform our understanding of the human genome and its impact on health, disease, and biotechnology. By leveraging state-of-the-art neural architectures and vast public genomic datasets, AlphaGenome delivers unprecedented insight into how genetic variants—both common and rare—affect gene regulation across the entire genome, not just the well-studied protein-coding regions that make up a mere 2 percent of our DNA. What is AlphaGenome? AlphaGenome is an advanced AI model developed by Google DeepMind, designed to predict how genetic variants impact gene regulation and other molecular processes at base-pair resolution across the entire genome. Unlike previous models that focused primarily on protein-coding DNA, AlphaGenome analyzes both coding and non-coding regions, offering a unified framework for interpreting the regulatory landscape of human genetics. Key highlights: Processes up to 1 million base pairs of DNA at once. Predicts thousands of molecular modalities, including gene expression, chromatin accessibility, RNA splicing, and protein binding. Integrates convolutional neural networks (CNNs) and transformers for both local motif detection and long-range genomic interactions. Trained on large-scale, multi-omic datasets (ENCODE, GTEx, 4D Nucleome, FANTOM5). Available via an API for non-commercial research, with plans for broader release. The need for advanced genomic AI The complexity of the human genome The human genome is a vast instruction manual, with over 3 billion DNA letters (base pairs). While only about 2 percent of these code for proteins, the remaining 98 percent—the non-coding regions—play crucial roles in regulating gene activity, determining when and where genes are turned on or off, and influencing susceptibility to diseases. Challenges in genomic interpretation: Variant effect prediction: Small changes (variants) in DNA can have profound or negligible effects, depending on their context. Non-coding regions: Most disease-associated variants identified by genome-wide association studies (GWAS) lie outside protein-coding regions, making their functional consequences difficult to interpret. Data volume: The scale and complexity of genomic data require models that can process long sequences and integrate diverse molecular signals. AlphaGenome was developed to address these challenges, providing a comprehensive, high-resolution view of how genetic variation shapes biology. Technical architecture of AlphaGenome Unified model for sequence-to-function prediction AlphaGenome's architecture is a hybrid neural network that combines the strengths of convolutional layers and transformer modules: Convolutional Neural Networks (CNNs): Detect short, local sequence motifs—such as transcription factor binding sites—by scanning DNA for recurring patterns. Transformers: Capture long-range dependencies and interactions between distant genomic elements, essential for modeling regulatory networks that span thousands of base pairs. This design enables AlphaGenome to analyze up to 1 million base pairs in a single pass, providing base-resolution predictions across vast genomic regions. Efficient training and inference Trained on Tensor Processing Units (TPUs), AlphaGenome achieves high computational efficiency, completing full model training in just four hours—using half the compute budget of its predecessor, Enformer. The model's architecture and data pipelines are optimized for both speed and accuracy, allowing rapid hypothesis generation and variant scoring at scale. Training data and benchmark performance Multi-omic datasets AlphaGenome's predictive power is rooted in its exposure to diverse, high-quality datasets: ENCODE: Comprehensive maps of functional elements in the genome. GTEx: Gene expression data across tissues. 4D Nucleome: Insights into genome structure and organization. FANTOM5: Transcriptional activity data. Benchmarking results Outperformed or matched specialized models in 24 out of 26 benchmark tests for variant effect prediction. Demonstrated superior performance in predicting regulatory effects, RNA splicing, and chromatin accessibility. Achieved state-of-the-art results in both single-sequence and variant effect prediction tasks. Key features and innovations Comprehensive variant effect prediction AlphaGenome can score both common and rare variants across the genome, including: Non-coding regulatory regions: Where most disease-associated variants reside. Protein-coding regions: Complementing tools like AlphaMissense. Multi-modal, base-resolution output Provides predictions for thousands of molecular properties at single-base resolution, enabling fine-grained analysis of genetic changes. Models RNA splice junctions directly—a critical advance for understanding diseases caused by splicing errors. Long-range genomic context Captures interactions between distant regulatory elements, such as enhancers and promoters, which are essential for accurate gene regulation modeling. Efficient, scalable, and accessible Trained efficiently on TPUs, with rapid inference capabilities. Available via API for non-commercial research, democratizing access for scientists worldwide. Applications in genomic research Decoding the non-coding genome AlphaGenome's ability to interpret the 98 percent of the genome that does not code for proteins opens new avenues for: Identifying regulatory variants that influence gene expression and disease risk. Prioritizing candidate variants in genome-wide association studies (GWAS). Understanding tissue-specific gene regulation and its disruption in disease. Functional genomics and hypothesis generation Researchers can use AlphaGenome to: Predict the impact of specific mutations before experimental validation. Generate functional hypotheses at scale, accelerating discovery in genetics and molecular biology. Impact on disease understanding and precision medicine From variant to function to disease AlphaGenome bridges the gap between genetic variation and biological function, providing insights that are crucial for: Rare disease diagnosis: Interpreting the effects of unique or de novo variants in patients with undiagnosed conditions. Cancer genomics: Understanding how somatic mutations in regulatory regions drive tumorigenesis. Pharmacogenomics: Predicting individual responses to drugs based on regulatory variants. Toward personalized medicine By enabling accurate prediction of variant effects across tissues and cell types, AlphaGenome supports the development of personalized therapies and precision diagnostics tailored to each individual's unique genetic makeup. Read more: UAE healthcare sector aims for 20 percent carbon emission reduction by 2030: Report Synthetic biology and beyond Designing synthetic DNA AlphaGenome's predictive capabilities extend to synthetic biology, where researchers aim to design custom DNA sequences with desired regulatory properties: Synthetic promoters and enhancers: Engineering regulatory elements for gene therapy or industrial biotechnology. Genome editing: Anticipating the consequences of CRISPR and other genome-editing interventions. Expanding to other species DeepMind has indicated plans to extend AlphaGenome's framework to new species , facilitating comparative genomics and cross-species functional annotation. AlphaGenome vs. previous models Feature AlphaGenome Enformer (2022) AlphaMissense (2023) Sequence length Up to 1 million bp Up to 200,000 bp N/A (missense focus) Coding & non-coding regions Yes Yes Coding only Variant effect prediction Yes (all regions) Limited Missense only Multi-modal output Thousands of types Dozens Protein function Splice junction modeling Direct Indirect No Training efficiency 4 hours on TPUs 8+ hours N/A Benchmark performance 24/26 top scores 18/26 N/A AlphaGenome represents a substantial leap in both scale and accuracy compared to previous models, especially in non-coding variant interpretation and multi-modal prediction. Ethical, societal, and clinical considerations Interpretability and trust As AI models become central to genomic interpretation, issues of transparency, explainability, and clinical validation are paramount. AlphaGenome's predictions must be interpreted within the context of experimental evidence and patient care, with careful attention to: False positives/negatives in variant effect prediction. Equity and access to advanced genomic tools across different populations and healthcare systems. Data privacy and security Handling genomic data raises significant privacy concerns, necessitating robust safeguards for patient information and compliance with global regulations. The human element As noted by AI alignment researchers, the psychological and informational context in which genomic insights are delivered is as important as their technical accuracy. AI must support clinicians in providing clear, compassionate communication to patients. The road ahead: Future developments Clinical integration DeepMind plans to extend AlphaGenome for clinical applications, including fine-tuning for disease-specific tasks, integration with electronic health records, and support for clinical decision-making. Expansion to other organisms and modalities Ongoing work aims to adapt AlphaGenome for other species and new molecular phenotypes, broadening its impact across biology and medicine. Open science and collaboration By making AlphaGenome available via API for non-commercial research, DeepMind promotes global collaboration and accelerates discovery in genomics. Final word AlphaGenome marks a new era in computational genomics, offering a unified, scalable, and accurate framework for interpreting the functional consequences of genetic variation across the entire genome. Its release in 2025 represents a milestone not just for AI and genomics, but for the broader quest to understand the language of life and harness it for human health, disease prevention, and biotechnological innovation.
Emirates 24/7
an hour ago
- Emirates 24/7
Is It Time to Rethink What Your Savings Are Really Doing?
In an economy where everyday expenses continue to climb, relying on traditional savings alone may no longer be enough. While putting money aside offers a sense of security, it often fails to keep pace with inflation - meaning that, over time, what feels like saving can quietly turn into losing. This growing awareness is prompting a shift in financial behaviour. More people are now looking for ways to make their money work harder, without diving into the complexities of high-risk investments or committing large sums they can't afford to lock away. Bridging the Gap Between Saving and Investing Digital investment platforms are emerging as practical solutions for individuals seeking balance: a way to preserve and grow wealth without becoming experts, and without having to set aside large sums of money. Among these is investnation, a UAE-based platform offering a low-barrier entry point into investing. With a minimum starting amount of AED 500, the platform is designed for everyday savers looking for stable returns without the risk of markets. Features Designed for Simplicity and Control: • Annual returns of up to 7%, offering potential growth beyond traditional savings • No lock-in period, allowing users to withdraw funds anytime, without penalties • Low-risk approach, ideal for those who prioritize consistency and predictability • Fully digital experience, with account setup and management available via mobile app Follow Emirates 24|7 on Google News.
Zawya
an hour ago
- Zawya
77% of South Africa businesses AI-ready, over half see benefits
A new Asus survey reveals that 77% of South African business decision-makers are ready to adopt AI tools immediately, with over half already experiencing measurable outcomes. These findings point to a tech-savvy market that is not just exploring AI, but actively using it to improve productivity, sharpen decision-making and enhance customer experiences. The results come from the Asus 2025 Future of SMBs report, which surveyed South African business leaders on how small and midsize businesses (SMBs) are navigating digital transformation. The findings reflect not only a strong appetite for innovation but also growing confidence in AI's real-world impact. 'There's real momentum behind AI adoption in South Africa. Instead of holding back, businesses are embracing these tools to solve real challenges and unlock new opportunities,' says Marce Heath, business head of marketing at Asus South Africa. South African SMBs lead in AI readiness The survey reveals a market that is both confident in technology and pragmatic about its potential: - 77% are ready to adopt AI immediately - 51% are already seeing clear business outcomes - 43% expect to see benefits within 1–2 years - 92% agree that adopting AI helps retain younger employees Respondents cited the following advantages: - Improved productivity and operational efficiency (76%) - Stronger data insights and analytics (67%) - Faster, more informed decision-making (54%) Even with this optimism, South African SMBs are approaching adoption strategically. Data security, system maintenance and the need to stay current with emerging technologies remain key concerns. This points to a preference for sustainable, long-term solutions over quick fixes. The South African findings align with broader insights from Asus's global white paper, Why SMBs Are Backing AI for Global Growth, based on research in 32 markets. Around the world, SMBs are leveraging AI to expand into new markets, navigate linguistic and operational barriers, and build long-term partnerships rather than short-term transactional relationships. Rather than chasing scale for its own sake, many are opting for controlled, tech-enabled growth, with AI and trusted digital infrastructure playing a central role in helping businesses adapt with confidence. Asus devices designed for the AI era To support this shift, Asus has developed laptops and desktops tailored to the demands of intelligent, connected work. The Asus Expert P Series combines enterprise-grade security with collaboration-enabling features, such as real-time translation, automated meeting summaries and transcription, watermarking and smart system optimisation. Powered by Asus AI ExpertMeet and Windows 11 Pro, these tools are helping teams collaborate securely, work more efficiently across time zones and adapt to hybrid work environments with ease. 'Technology should accelerate growth, not complicate it,' adds Heath. 'With our AI-ready devices, we're equipping South African businesses with practical, secure tools that support how they work today and into the future.' It's this blend of performance, usability and peace of mind that makes Asus devices a natural fit for the AI-driven priorities emerging across the local business landscape. Behind the insights: Inside the Asus SMB study The Asus 2025 Future of SMBs report surveyed 101 South African business leaders across industries, including general managers, IT specialists and procurement professionals. It forms part of a broader global study conducted across 32 markets to understand how SMBs are responding to technological change. To access the full Asus 2025 Future of SMBs report or learn more about Asus Business, visit SMB AI white paper. For media inquiries Clockwork contact: Tshepi@ Asus contact: marce_heath@ About Asus Asus is a global technology brand that provides innovative and intuitive devices, components and solutions to deliver incredible experiences that enhance the lives of people everywhere. With its team of 5,000 in-house R&D experts, the company is world-renowned for continuously reimagining today's technologies. Consistently ranked as one of Fortune's World's Most Admired Companies, Asus is also committed to sustaining an incredible future. The goal is to create a net zero enterprise that helps drive the shift towards a circular economy, with a responsible supply chain creating shared value for every one of us.
