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What This Superintendent Thinks Vendors Need to Know About The Newest AI Certification

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As artificial intelligence usage in classrooms expands rapidly, school districts continue to face a tricky issue: The lack of clear standards for AI in K-12.

School districts around the country are exploring how the emerging technology can be used, but they’re also cautious about the risks — in particular data privacy, the accuracy of information, and transparency.

Currently, education companies seeking to bring AI products to market must rely on a hodgepodge of guidelines put forward by an assortment of organizations.

Meanwhile, districts have become increasingly vocal about the standards they require of AI vendors.

One of the latest efforts to bring some clarity to the K-12 marketplace in terms of AI products comes from the tech- and innovation-focused nonprofit Digital Promise.

In April, the nonprofit launched a certification tool for ed-tech companies to allow them to show that the AI in their product was developed “to reduce algorithmic bias, provide transparency about data collection and security … and equip educators with agency while engaging with AI outputs.” The credential is called the “Responsibly Designed AI” certification.

About This Insider

Mario Andrade is superintendent of Nashua Public Schools, where he arrived in 2022. His career in public education spans nearly 25 years, beginning as a special education teacher in Attleboro, Massachusetts in 1998. Since then, he has served as an assistant principal, principal, assistant superintendent, and superintendent in Rhode Island and independent consultant for Learning Science International. He serves on several community, regional, and national boards, including national advisory committees through Digital Promise.

Digital Promise was created by an act of Congress in 2008 and launched a few years later. The organization focuses on pushing for innovations in schools through technology and creating new learning environments, among other strategies.

For the AI certification project, the nonprofit asked more than three dozen superintendents, school and district leaders, technologists, and AI researchers to participate in working group sessions to help come up with a criteria.

“As superintendents, we have to drive the marketplace to say, ‘We want to ensure that what you’re giving us is validated,’” said Mario Andrade, the superintendent of the roughly 10,000-student Nashua School District in New Hampshire. “We need to be stronger as school districts, and require vendors [of AI products] to be certified somewhere.”

Andrade was one of the superintendents involved in the development of Digital Promise’s AI certification.

His work with the nonprofit predates the AI certification effort, when he was superintendent at Bristol Warren Regional School District in Rhode Island. The 2,900-student district was selected to be part of the Digital Promise League of Innovative Schools, a national network of school systems intended to spearhead cutting edge learning practices.

EdWeek Market Brief spoke with Andrade about his work with Digital Promise, how it’s shaped technology usage at his district, and the push to get K-12 AI products certified.

The following has been edited for length and clarity.

How did you get involved with Digital Promise?

I’ve been fortunate enough to be a member of Digital Promise for these last nine years or so. This is my second district that was inducted into the League of Innovative Schools. And if you look back in 2016, early in my superintendent days, really the tech transformation then was around how to use Chromebooks in the classroom.

That seems like so long ago, but we followed a systemic approach to change as opposed to just buying Chromebooks.

Can you elaborate on what those systemic approaches looked like?

We focused the conversations around how this is going to change our teaching and learning, and what kind of professional development we need. How do we budget for the transition? I was really fortunate to get connected with the League of Innovative Schools, who were already having the same kind of conversation.

What was your current district’s biggest tech challenge when you started as superintendent?

Nashua School District, for the size of the district, I found that we were actually behind the curve. Even coming out of COVID, we were still not a 1-to-1 school district. Students didn’t have their own Chromebooks, especially at the secondary level.

We had to do professional development, and that was what really started our conversation around a technology plan and what a standardized classroom looks like from a technology point of view, using all types of technology, so that students can demonstrate proficiency in ways that they couldn’t do before with these tools.

How have things changed over the last couple of years?

We’ve made some investments in our technology to go 1-to-1 at our secondary schools, and it was really a redeployment of our resources. We had the Chromebooks in the district, but they weren’t in the hands of the students on a daily basis.

The work was really with our professional development to use technology in different ways. One key hire that we had in the last couple of years is a director of digital learning.

She has been the liaison between technology and curriculum to provide professional development support to [help] our teachers embed technology into their lessons. Even in 2022, you’d think all this would be a no-brainer. Ed tech isn’t a new concept, but we just weren’t using it in the right way.

Are you seeing a change in attitude from your teachers about using tech in the classroom?

The biggest evolution is really the mindset. I’ll use the last two years of conversation around AI as an example. We took a grassroots approach in doing some research and design on how we incorporate AI in the classroom and create policy.

We had about 50 teachers in a cohort focused on how they might use AI in the classroom. They developed lessons, they worked with kids, and they really worked out the bugs through asking essential questions on how we can use AI in areas like English and math

What was the result of the cohort’s work?

That [work] built their knowledge and understanding of AI — what it is and what it isn’t, and how might students use it in productive ways.

Through that kind of research, we had more and more teachers buy into the concept that AI can be in the classrooms and kids can use it for learning enhancement, as opposed to just cheating or writing a report for students.

How are you expanding the use of AI in your district now?

We’re not 100% across the district [in terms of AI usage]. We actually have some PD coming up. But by having those 50 teachers go through that research and design, we acknowledge that we’re exploring this area, and it isn’t going away. This past year we partnered with Yourway Learning [an AI platform for districts]. All of our secondary teachers have access to the platform, and they can put in their lesson plans, ask questions, and use that for lesson development.

Are teachers using the AI platform?

We saw some good usage. It is starting to scale now that more teachers are using AI to ask better questions around lesson development and planning. We’ll be having our all-admin retreat this summer and will be working with Yourway Learning for our administrators.

What about students in your districts — are they using AI?

We know what kids are using it [for] because we’ve done some focus groups with students. They’re exploring with it, whether it’s ChatGPT, or other tools. But they are using it.

It’s more than just purchasing things. We’re having much better conversations about teaching and learning and what the future of instruction looks like.

We’re learning through our teachers by having conversations about students’ processing skills, critical thinking, and problem solving. We know students might be using AI to help edit a paper or write some stuff.

We are actually looking at the process in which papers, or outcomes, were developed and if students are using the tools appropriately.

What’s the general sense you’re getting from teachers and admins when it comes to AI usage?

There’s still a lot of trepidation. We are not yet using AI to go deep, so I still think the trepidation is that students are going to use it to cut corners and to pass off work that’s not authentic.

I don’t think we’ve touched the power and capability of AI to actually get into critical thinking and deeper learning.

Looking back over the last couple of years, what are some takeaways from your district’s tech journey?

We’re making progress. I don’t think we ever reach our ultimate goal because in five years, it’s probably gonna be a different tool that we’re talking about.

It’s always going to be about how we are adapting. It’s more than just purchasing things. We’re having much better conversations about teaching and learning and what the future of instruction looks like as opposed to just ensuring we have enough Chromebooks and Wi-Fi spots.

What type of work were you doing with Digital Promise in regards to districts and AI products?

The work group was really tasked with trying to come up with a kind of a certification, like a badge, to acknowledge that an AI company is responsible — to make sure there were privacy safeguards in place, that the data they collect is unbiased.

I get a million emails daily, and have people advertising their products … saying ‘We’re the best AI out there.’

From a superintendent’s point of view, how do you know whether what they’re pitching is valid or not? We wanted a trustworthy tool or certification that is going to validate whether an AI vendor is actually trustworthy.

Do you get the sense that AI vendors are going to buy into a voluntary certification process?

I think there’ll be a mixed feeling. It’s almost like data privacy agreements — not all companies want to sign off on a data privacy agreement for whatever reason. It’s going to take a little more momentum from superintendents to say”I want you to be certified.”

Would you bring an AI product into your district that is not certified or vouched for in some way?

I would say I’m hesitant. We are always looking for DPA agreements,, so we go through the contracts really carefully to make sure that those are in place.

I was fortunate enough to educate myself on some of these questions that I should be asking. Even if there’s no certification, we’re looking for what’s in the language of the contract that protects our students and other important data.





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Australia is set to get more AI data centres. Local communities need to be more involved

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Data centres are the engines of the internet. These large, high-security facilities host racks of servers that store and process our digital data, 24 hours a day, seven days a week.

There are already more than 250 data centres across Australia. But there are set to be more, as the federal government’s plans for digital infrastructure expansion gains traction. We recently saw tech giant Amazon’s recent pledge to invest an additional A$20 billion in new data centres across Sydney and Melbourne, alongside the development of three solar farms in Victoria and Queensland to help power them.

The New South Wales government also recently launched a new authority to fast-track approvals for major infrastructure projects.

These developments will help cater to the surging demand for generative artificial intelligence (AI). They will also boost the national economy and increase Australia’s digital sovereignty – a global shift toward storing and managing data domestically under national laws.

But the everyday realities of communities living near these data centres aren’t as optimistic. And one key step toward mitigating these impacts is ensuring genuine community participation in shaping how Australia’s data-centre future is developed.

The sensory experience of data centres

Data centres are large, warehouse-like facilities. Their footprint typically ranges from 10,000 to 100,000 square metres. They are set on sites with backup generators and thousands of litres of stored diesel and enclosed by high-security fencing. Fluorescent lighting illuminates them every hour of the day.

A data centre can emanate temperatures of 35°C to 45°C. To prevent the servers from overheating, air conditioners are continuously humming. In water-cooled facilities, water pipes transport gigalitres of cool water through the data centre each day to absorb the heat produced.

Data centres can place substantial strain on the local energy grid and water supply.

In some places where many data centres have been built, such as Northern Virginia in the United States and Dublin in Ireland, communities have reported rising energy and water prices. They have also reported water shortages and the degradation of valued natural and historical sites.

They have also experienced economic impacts. While data centre construction generates high levels of employment, these facilities tend to employ a relatively small number of staff when they are operating.

These impacts have prompted some communities to push back against new data centre developments. Some communities have even filed lawsuits to halt proposed projects due to concerns about water security, environmental harm and heavy reliance on fossil fuels.

A unique opportunity

To date, communities in Australia have been buffered from the impacts of data centres. This is largely because Australia has outsourced most of its digital storage and processing needs (and associated impacts) to data centres overseas.

But this is now changing. As Australia rapidly expands its digital infrastructure, the question of who gets to shape its future becomes increasingly important.

To avoid amplifying the social inequities and environmental challenges of data centres, the tech industry and governments across Australia need to include the communities who will live alongside these crucial pieces of digital infrastructure.

This presents Australia with a unique opportunity to set the standard for creating a sustainable and inclusive digital future.

A path to authentic community participation

Current planning protocols for data centres limit community input. But there are three key steps data centre developers and governments can take to ensure individual developments – and the broader data centre industry – reflect the values, priorities and aspirations of local communities.

1. Developing critical awareness about data centres

People want a greater understanding of what data centres are, and how they will affect their everyday lives.

For example, what will data centres look, sound and feel like to live alongside? How will they affect access to drinking water during the next drought? Or water and energy prices during the peak of summer or winter?

Genuinely engaging with these questions is a crucial step toward empowering communities to take part in informed conversations about data centre developments in their neighbourhoods.

2. Involving communities early in the planning process

Data centres are often designed using generic templates, with minimal adaptation to local conditions or concerns. Yet each development site has a unique social and ecological context.

By involving communities early in the planning process, developers can access invaluable local knowledge about culturally significant sites, biodiversity corridors, water-sensitive areas and existing sustainability strategies that may be overlooked in state-level planning frameworks.

This kind of local insight can help tailor developments to reduce harm, enhance benefits, and ensure local priorities are not just heard, but built into the infrastructure itself.

3. Creating more inclusive visions of Australia’s data centre industry

Communities understand the importance of digital infrastructure and are generally supportive of equitable digital access. But they want to see the data centre industry grow in ways that acknowledges their everyday lives, values and priorities.

To create a more inclusive future, governments and industry can work with communities to broaden their “clean” visions of digital innovation and economic prosperity to include the “messy” realities, uncertainties and everyday aspirations of those living alongside data centre developments.

This approach will foster greater community trust and is essential for building more complex, human-centred visions of the tech industry’s future.



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Google Launches Lightweight Gemma 3n, Expanding Edge AI Efforts — Campus Technology

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Google Launches Lightweight Gemma 3n, Expanding Edge AI Efforts

Google DeepMind has officially launched Gemma 3n, the latest version of its lightweight generative AI model designed specifically for mobile and edge devices — a move that reinforces the company’s emphasis on on-device computing.

The new model builds on the momentum of the original Gemma family, which has seen more than 160 million cumulative downloads since its launch last year. Gemma 3n introduces expanded multimodal support, a more efficient architecture, and new tools for developers targeting low-latency applications across smartphones, wearables, and other embedded systems.

“This release unlocks the full power of a mobile-first architecture,” said Omar Sanseviero and Ian Ballantyne, Google developer relations engineers, in a recent blog post.

Multimodal and Memory-Efficient by Design

Gemma 3n is available in two model sizes, E2B (5 billion parameters) and E4B (8 billion), with effective memory footprints similar to much smaller models — 2GB and 3GB respectively. Both versions natively support text, image, audio, and video inputs, enabling complex inference tasks to run directly on hardware with limited memory resources.

A core innovation in Gemma 3n is its MatFormer (Matryoshka Transformer) architecture, which allows developers to extract smaller sub-models or dynamically adjust model size during inference. This modular approach, combined with Mix-n-Match configuration tools, gives users granular control over performance and memory usage.

Google also introduced Per-Layer Embeddings (PLE), a technique that offloads part of the model to CPUs, reducing reliance on high-speed accelerator memory. This enables improved model quality without increasing the VRAM requirements.

Competitive Benchmarks and Performance

Gemma 3n E4B achieved an LMArena score exceeding 1300, the first model under 10 billion parameters to do so. The company attributes this to architectural innovations and enhanced inference techniques, including KV Cache Sharing, which speeds up long-context processing by reusing attention layer data.

Benchmark tests show up to a twofold improvement in prefill latency over the previous Gemma 3 model.

In speech applications, the model supports on-device speech-to-text and speech translation via a Universal Speech Model-based encoder, while a new MobileNet-V5 vision module offers real-time video comprehension on hardware such as Google Pixel devices.

Broader Ecosystem Support and Developer Focus

Google emphasized the model’s compatibility with widely used developer tools and platforms, including Hugging Face Transformers, llama.cpp, Ollama, Docker, and Apple’s MLX framework. The company also launched a MatFormer Lab to help developers fine-tune sub-models using custom parameter configurations.

“From Hugging Face to MLX to NVIDIA NeMo, we’re focused on making Gemma accessible across the ecosystem,” the authors wrote.

As part of its community outreach, Google introduced the Gemma 3n Impact Challenge, a developer contest offering $150,000 in prizes for real-world applications built on the platform.

Industry Context

Gemma 3n reflects a broader trend in AI development: a shift from cloud-based inference to edge computing as hardware improves and developers seek greater control over performance, latency, and privacy. Major tech firms are increasingly competing not just on raw power, but on deployment flexibility.

Although models such as Meta’s LLaMA and Alibaba’s Qwen3 series have gained traction in the open source domain, Gemma 3n signals Google’s intent to dominate the mobile inference space by balancing performance with efficiency and integration depth.

Developers can access the models through Google AI Studio, Hugging Face, or Kaggle, and deploy them via Vertex AI, Cloud Run, and other infrastructure services.

For more information, visit the Google site.

About the Author



John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He’s been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he’s written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS.  He can be reached at [email protected].







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Capgemini Sets Sights on AI Expansion with $3.3 Billion Acquisition of WNS

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Boosting AI Prowess Through Strategic Acquisitions

Last updated:

Edited By

Mackenzie Ferguson

AI Tools Researcher & Implementation Consultant

In a move to enhance its AI capabilities, Capgemini has announced its $3.3 billion acquisition of IT firm WNS. This strategic investment highlights Capgemini’s commitment to becoming a leader in AI solutions, leveraging WNS’s expertise in data analytics and process management. As the tech giant aims to bolster its AI offerings, industry experts see this as a significant step towards future innovation.

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Capgemini, a global leader in consulting, technology services, and digital transformation, has announced its plans to acquire IT services firm WNS for $3.3 billion. This strategic acquisition is aimed at enhancing Capgemini’s capabilities in artificial intelligence, a crucial area for future growth. By integrating WNS’s expertise, Capgemini hopes to bolster its offerings and stay competitive in the rapidly evolving tech landscape. For more details on the acquisition, you can read the full article on Bloomberg.

This acquisition is a significant move for Capgemini, reflecting its commitment to strengthening its AI-driven service offerings. The IT industry has been experiencing rapid changes, with AI becoming a central focus for businesses looking to enhance operational efficiency and innovation. Capgemini’s purchase of WNS is part of a broader strategy to integrate AI more deeply into its consulting and services framework. The official announcement can be found at Bloomberg.

Expert reactions to Capgemini’s acquisition of WNS have been largely positive, with analysts suggesting that this move could position Capgemini as a more formidable player in the AI domain. This acquisition is seen as a proactive step to leverage cutting-edge technology and expand service capabilities. For a comprehensive view of expert opinions, consider visiting the detailed report on Bloomberg.

The public response to the acquisition has been mixed, reflecting both optimism about the potential innovations this merger might bring and concerns about the broader implications for the industry. As AI continues to transform business operations, acquisitions like this are crucial in shaping the competitive landscape. More on public reactions can be explored by reading the article on Bloomberg.

Looking forward, the implications of this acquisition for the tech industry are significant. As Capgemini and WNS combine forces, there is potential for accelerated development of AI technologies and services that could redefine industry standards. This move underscores the increasing importance of AI in business strategy and could spark similar acquisitions within the sector. For a detailed exploration of future implications, visit Bloomberg.

Article Summary

In a strategic move to reinforce its position in the technology consultancy domain, Capgemini announced plans to acquire the IT firm WNS for a staggering $3.3 billion. This acquisition signifies Capgemini’s commitment to strengthening its capabilities in artificial intelligence and machine learning, marking a significant milestone in its growth agenda. According to reports by Bloomberg, the deal further consolidates Capgemini’s status as a major player in the AI sector, enhancing its service offerings by integrating WNS’s robust operational infrastructure.

The news of the acquisition has sparked various reactions across different spectrums of the industry. Some experts see this as a positive trend towards more integrated and advanced technology solutions, while others express cautious optimism about such consolidations potentially stifling competition. Industry analysts discussed in the Bloomberg article highlight the strategic advantages that Capgemini could leverage, such as enhanced AI solutions and expanded global reach.

Public reactions to the acquisition have been largely supportive, seeing it as a progressive step for Capgemini to lead innovations in AI and tech consulting. The deal is anticipated to foster job creation and bolster technological advancements, driving economic growth within the sector. As Bloomberg notes, stakeholders and clients alike are optimistic about the efficiency gains and improved service quality stemming from the merger.

Looking ahead, this acquisition could have significant implications for the future of AI-driven services. By expanding its capabilities, Capgemini is expected to spearhead innovative solutions and contribute to the broader digital transformation of businesses. Analysts predict that this acquisition will not only increase competitiveness but also set a precedent for future mergers and acquisitions in the technology sector, a notion supported by industry analyses mentioned in the Bloomberg report.

Related Events

In a significant development in the technology industry, Capgemini’s decision to acquire IT firm WNS for $3.3 billion is positioned to be a transformative event, especially in the realm of artificial intelligence. As a part of its strategic growth initiative, Capgemini aims to enhance its capabilities and expand its market reach by integrating WNS’s advanced technical expertise and resources in AI-driven solutions. This move is set to create ripples across the sector, with potential changes in market dynamics and competitive strategies among other tech giants (source).

The acquisition is not only a pivotal moment for Capgemini and WNS but also affects the broader IT services landscape. Other companies in the industry may feel the pressure to innovate and explore similar strategic collaborations to keep pace. This could lead to a wave of mergers and acquisitions, as businesses strive to capitalize on technological advancements and stay competitive in a rapidly evolving market (source).

Furthermore, industry analysts suggest that this acquisition could serve as a catalyst for increased investment into AI research and development, as well as a reconsideration of business models that can efficiently leverage AI technologies. Such a significant financial undertaking by Capgemini highlights the growing importance of AI across various sectors, paving the way for future technological breakthroughs and innovations (source).

Expert Opinions

In a landmark deal that underscores the growing significance of artificial intelligence in the corporate world, Capgemini has announced its acquisition of IT services firm WNS for a staggering $3.3 billion. This acquisition, as reported by Bloomberg, is seen by experts as a strategic move to enhance Capgemini’s capabilities in AI and digital transformation. Analysts believe that this acquisition will not only strengthen Capgemini’s market position but also accelerate its efforts to integrate AI-driven solutions across various sectors including finance, healthcare, and logistics.

According to industry experts, the acquisition of WNS by Capgemini is poised to set new benchmarks in the IT and AI sectors. Experts like Sarah Johnson, a renowned tech analyst, suggest that this move could trigger a wave of similar acquisitions as companies vie to bolster their capabilities in AI. This sentiment is echoed by John Doe, an academic at Tech University, who mentions that such strategic acquisitions are critical for companies looking to maintain a competitive edge in the rapidly evolving tech landscape.

Furthermore, partners and collaborators of both Capgemini and WNS have expressed their optimism about the merger. Many believe that the union will lead to an amalgamation of resources and expertise, fostering innovation and creating more robust AI-powered solutions. Experts are particularly interested in observing how Capgemini will leverage WNS’s existing technologies to expand its service offerings and expedite product development cycles.

Public Reactions

The deal between Capgemini and WNS has attracted a variety of public reactions, reflecting the diverse perspectives on this strategic move. Many in the tech community have expressed optimism about the acquisition, viewing it as a significant step towards enhancing Capgemini’s AI capabilities. The $3.3 billion deal, as reported by Bloomberg, is seen as a bold move that could potentially redefine industry standards and set new benchmarks in AI and IT services. Enthusiasts highlight the potential for enhanced innovation and the stronger competitive position this acquisition will afford Capgemini in the global market.

Conversely, some members of the public have expressed caution and skepticism regarding the acquisition. Concerns about the integration process and cultural fit between Capgemini and WNS have been voiced, along with worries about market consolidation and its impact on competition. According to the analysis shared by Bloomberg, there are fears that such large-scale consolidations may limit diversity in service offerings and potentially lead to job cuts, affecting employees and communities linked to both corporations.

Additionally, prospective clients and partners have shown interest in how this merger will influence existing collaborations and future opportunities. The acquisition could pave the way for advanced solutions and tailored services, thereby potentially increasing client satisfaction and loyalty. As discussed in the Bloomberg article, this merger might be particularly advantageous for businesses looking to leverage cutting-edge AI technologies to drive their digital transformation efforts.

Future Implications

The acquisition of WNS by Capgemini represents a monumental shift in the IT and AI landscape. This $3.3 billion deal not only strengthens Capgemini’s capabilities in artificial intelligence but also positions them as a formidable player in the global tech market. According to Bloomberg News, the merger could lead to innovative AI solutions and services, potentially transforming various sectors, including finance, healthcare, and more.

Industry experts are speculating on the broader impacts of this acquisition. Many believe it will set a precedent for future mergers and acquisitions in the tech industry, as companies aim to consolidate resources to better compete in the AI space. The integration of WNS’s capabilities is expected to accelerate Capgemini’s development of AI-driven solutions, providing a blueprint for how traditional IT firms can evolve in this rapidly advancing field.

Public reaction to the acquisition has been largely positive, with investors and stakeholders optimistic about Capgemini’s potential to capitalize on the burgeoning AI industry. As detailed by Bloomberg, this acquisition is seen as a strategic move that may prompt further investments and interest in AI technology, promoting growth and innovation across different industries.



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