A recent study questions if large language models (LLMs) truly form coherent world models, despite their accurate outputs in complex tasks like generating directions or playing games. Researchers found that while LLMs provide nearly flawless driving directions, they fail with unexpected changes, suggesting the models don't grasp underlying rules. 
Disney+ heeft in november zin in films. Deze maand verschijnen namelijk maar liefst twee documentaires – waaronder één over componist John Williams – een kerstfilm en een dramafilm! Maar natuurlijk zijn er ook weer wat nieuwe series in aantocht.
Kerstmis en Disney, die twee gaan natuurlijk hand in hand. Daarmee is het geen verrassing dat Disney+ half november een nieuwe kerstfilm uitbrengt. Het gaat om An Almost Christmas Story, de derde en laatste korte film van producent Alfonso Cuarón. De film is gebaseerd op een waargebeurd verhaal.
In 2020 werd een kleine uil gered uit de kerstboom op Rockefeller Center in New York. De film gaat over deze uil, genaamd Moon. Terwijl hij probeert om de drukke stad te ontsnappen, wordt hij vrienden met een klein, verdwaald meisje genaamd Luna. Samen gaan ze op avontuur, in de hoop hun ouders terug te vinden. Tijdens die reis ontdekken ze de magie van het kerstseizoen.
Ook zie je deze maand de dramafilm Out of My Mind, over Melody Brooks. Zij is heeft scherpe humor en een net zo scherpe geest, maar heeft een hersenaandoening. Daardoor is ze verlamd, kan ze niet praten en zit ze in een rolstoel. Met als gevolg dat ze niet dezelfde kansen krijgt als haar leeftijdsgenoten.
Als een jonge leerkracht haar potentieel ontdekt, krijgt Melody de kans om mee te doen aan het gewone onderwijs. Daar laat ze zien dat was zij te zeggen heeft, veel belangrijker is dan hoe ze het precies zegt.
Deze films komen in november naar Disney+:
Ook verschijnen er weer diverse series op Disney+ in november. Denk bijvoorbeeld aan Gangnam B-Side, een Zuid-Koreaanse serie die zich in Seoul afspeelt. We volgen Jae-Hee, die in een bekende bar werkt. Zij heeft kennis van een geheim dat in verbinding staat met een reeks verdwijningen, maar verdwijnt vervolgens zelf ook. Detective Kang, de vogelvrije Yoon en advocaat Min proberen daarop ieder achter de waarheid te komen.
Ook verschijnt deze maand de thrillerserie Say Nothing. Deze serie gaat over de alleenstaande moeder Jean McConville, die in 1972 in Belfast werd ontvoerd en nooit meer levend werd teruggezien. De serie laat de gevolgen daarvan zien door de ogen van twee zussen, een militair strateeg en de naar vrede strevende Gerry Adams.
Deze series komen in november naar Disney+
Lees Nieuw op Disney+ in november: An Almost Christmas Story en Say Nothing verder op Numrush
Today, Apple released iOS 18.1, iPadOS 18.1, macOS Sequoia 15.1, tvOS 18.1, visionOS 2.1, and watchOS 11.1. The iPhone, iPad, and Mac updates are focused on bringing the first AI features the company has marketed as "Apple Intelligence" to users.
Once they update, users with supported devices in supported regions can enter a waitlist to begin using the first wave of Apple Intelligence features, including writing tools, notification summaries, and the "reduce interruptions" focus mode.
In terms of features baked into specific apps, Photos has natural language search, the ability to generate memories (those short gallery sequences set to video) from a text prompt, and a tool to remove certain objects from the background in photos. Mail and Messages get summaries and smart reply (auto-generating contextual responses).
© Apple
The chatbot revolution has left our world awash in AI-generated text: It has infiltrated our news feeds, term papers, and inboxes. It’s so absurdly abundant that industries have sprung up to provide moves and countermoves. Some companies offer services to identify AI-generated text by analyzing the material, while others say their tools will “humanize“ your AI-generated text and make it undetectable. Both types of tools have questionable performance, and as chatbots get better and better, it will only get more difficult to tell whether words were strung together by a human or an algorithm.
Here’s another approach: Adding some sort of watermark or content credential to text from the start, which lets people easily check whether the text was AI-generated. New research from Google DeepMind, described today in the journal Nature, offers a way to do just that. The system, called SynthID-Text, doesn’t compromise “the quality, accuracy, creativity, or speed of the text generation,” says Pushmeet Kohli, vice president of research at Google DeepMind and a coauthor of the paper. But the researchers acknowledge that their system is far from foolproof, and isn’t yet available to everyone—it’s more of a demonstration than a scalable solution.
Google has already integrated this new watermarking system into its Gemini chatbot, the company announced today. It has also open-sourced the tool and made it available to developers and businesses, allowing them to use the tool to determine whether text outputs have come from their own large language models (LLMs), the AI systems that power chatbots. However, only Google and those developers currently have access to the detector that checks for the watermark. As Kohli says: “While SynthID isn’t a silver bullet for identifying AI-generated content, it is an important building block for developing more reliable AI identification tools.”
Content credentials have been a hot topic for images and video, and have been viewed as one way to combat the rise of deepfakes. Tech companies and major media outlets have joined together in an initiative called C2PA, which has worked out a system for attaching encrypted metadata to image and video files indicating if they’re real or AI-generated. But text is a much harder problem, since text can so easily be altered to obscure or eliminate a watermark. While SynthID-Text isn’t the first attempt at creating a watermarking system for text, it is the first one to be tested on 20 million prompts.
Outside experts working on content credentials see the DeepMind research as a good step. It “holds promise for improving the use of durable content credentials from C2PA for documents and raw text,” says Andrew Jenks, Microsoft’s director of media provenance and executive chair of the C2PA. “This is a tough problem to solve, and it is nice to see some progress being made,” says Bruce MacCormack, a member of the C2PA steering committee.
SynthID-Text works by discreetly interfering in the generation process: It alters some of the words that a chatbot outputs to the user in a way that’s invisible to humans but clear to a SynthID detector. “Such modifications introduce a statistical signature into the generated text,” the researchers write in the paper. “During the watermark detection phase, the signature can be measured to determine whether the text was indeed generated by the watermarked LLM.”
The LLMs that power chatbots work by generating sentences word by word, looking at the context of what has come before to choose a likely next word. Essentially, SynthID-Text interferes by randomly assigning number scores to candidate words and having the LLM output words with higher scores. Later, a detector can take in a piece of text and calculate its overall score; watermarked text will have a higher score than non-watermarked text. The DeepMind team checked their system’s performance against other text watermarking tools that alter the generation process, and found that it did a better job of detecting watermarked text.
However, the researchers acknowledge in their paper that it’s still easy to alter a Gemini-generated text and fool the detector. Even though users wouldn’t know which words to change, if they edit the text significantly or even ask another chatbot to summarize the text, the watermark would likely be obscured.
To be sure that SynthID-Text truly didn’t make chatbots produce worse responses, the team tested it on 20 million prompts given to Gemini. Half of those prompts were routed to the SynthID-Text system and got a watermarked response, while the other half got the standard Gemini response. Judging by the “thumbs up” and “thumbs down” feedback from users, the watermarked responses were just as satisfactory to users as the standard ones.
Which is great for Google and the developers building on Gemini. But tackling the full problem of identifying AI-generated text (which some call AI slop) will require many more AI companies to implement watermarking technologies—ideally, in an interoperable manner so that one detector could identify text from many different LLMs. And even in the unlikely event that all the major AI companies signed on to some agreement, there would still be the problem of open-source LLMs, which can easily be altered to remove any watermarking functionality.
MacCormack of C2PA notes that detection is a particular problem when you start to think practically about implementation. “There are challenges with the review of text in the wild,” he says, “where you would have to know which watermarking model has been applied to know how and where to look for the signal.” Overall, he says, the researchers still have their work cut out for them. This effort “is not a dead end,” says MacCormack, “but it’s the first step on a long road.”
New research shows that stories generated by AI, such as ChatGPT, are almost as good as those written by humans. However, when people are told a story is AI-generated, they rate it more negatively, revealing a bias against AI-created content. 
Researchers analyzed AI-generated and human-written college admissions essays, finding that AI-generated essays resemble those written by male students from privileged backgrounds. AI essays tended to use longer words and exhibited less variety in writing style than human essays, particularly resembling essays from private school applicants. The study highlights concerns about the use of AI in crafting admissions essays, as AI may dilute a student’s authentic voice. Students are encouraged to use AI as a tool to enhance, not replace, their personal narrative in writing. 
“What do I need for cold weather golf?”
“What are the differences between trail shoes and running shoes?”
“What are the best dinosaur toys for a five year old?”
These are some of the open-ended questions customers might ask a helpful sales associate in a brick-and-mortar store. But how can customers get answers to similar questions while shopping online?
Amazon’s answer is Rufus, a shopping assistant powered by generative AI. Rufus helps Amazon customers make more informed shopping decisions by answering a wide range of questions within the Amazon app. Users can get product details, compare options, and receive product recommendations.
I lead the team of scientists and engineers that built the large language model (LLM) that powers Rufus. To build a helpful conversational shopping assistant, we used innovative techniques across multiple aspects of generative AI. We built a custom LLM specialized for shopping; employed retrieval-augmented generation with a variety of novel evidence sources; leveraged reinforcement learning to improve responses; made advances in high-performance computing to improve inference efficiency and reduce latency; and implemented a new streaming architecture to get shoppers their answers faster.
Most LLMs are first trained on a broad dataset that informs the model’s overall knowledge and capabilities, and then are customized for a particular domain. That wouldn’t work for Rufus, since our aim was to train it on shopping data from the very beginning—the entire Amazon catalog, for starters, as well as customer reviews and information from community Q&A posts. So our scientists built a custom LLM that was trained on these data sources along with public information on the web.
But to be prepared to answer the vast span of questions that could possibly be asked, Rufus must be empowered to go beyond its initial training data and bring in fresh information. For example, to answer the question, “Is this pan dishwasher-safe?” the LLM first parses the question, then it figures out which retrieval sources will help it generate the answer.
Our LLM uses retrieval-augmented generation (RAG) to pull in information from sources known to be reliable, such as the product catalog, customer reviews, and community Q&A posts; it can also call relevant Amazon Stores APIs. Our RAG system is enormously complex, both because of the variety of data sources used and the differing relevance of each one, depending on the question.
Every LLM, and every use of generative AI, is a work in progress. For Rufus to get better over time, it needs to learn which responses are helpful and which can be improved. Customers are the best source of that information. Amazon encourages customers to give Rufus feedback, letting the model know if they liked or disliked the answer, and those responses are used in a reinforcement learning process. Over time, Rufus learns from customer feedback and improves its responses.
Rufus needs to be able to engage with millions of customers simultaneously without any noticeable delay. This is particularly challenging since generative AI applications are very compute-intensive, especially at Amazon’s scale.
To minimize delay in generating responses while also maximizing the number of responses that our system could handle, we turned to Amazon’s specialized AI chips, Trainium and Inferentia, which are integrated with core Amazon Web Services (AWS). We collaborated with AWS on optimizations that improve model inference efficiency, which were then made available to all AWS customers.
But standard methods of processing user requests in batches will cause latency and throughput problems because it’s difficult to predict how many tokens (in this case, units of text) an LLM will generate as it composes each response. Our scientists worked with AWS to enable Rufus to use continuous batching, a novel LLM technique that enables the model to start serving new requests as soon as the first request in the batch finishes, rather than waiting for all requests in a batch to finish. This technique improves the computational efficiency of AI chips and allows shoppers to get their answers quickly.
We want Rufus to provide the most relevant and helpful answer to any given question. Sometimes that means a long-form text answer, but sometimes it’s short-form text, or a clickable link to navigate the store. And we had to make sure the presented information follows a logical flow. If we don’t group and format things correctly, we could end up with a confusing response that’s not very helpful to the customer.
That’s why Rufus uses an advanced streaming architecture for delivering responses. Customers don’t need to wait for a long answer to be fully generated—instead, they get the first part of the answer while the rest is being generated. Rufus populates the streaming response with the right data (a process called hydration) by making queries to internal systems. In addition to generating the content for the response, it also generates formatting instructions that specify how various answer elements should be displayed.
Even though Amazon has been using AI for more than 25 years to improve the customer experience, generative AI represents something new and transformative. We’re proud of Rufus, and the new capabilities it provides to our customers.
In close with a dedicated force in our field who believes in the power of education to shape lives.
INTERVIEW | by Victor Rivero
As CEO of Savvas Learning Company, Bethlam Forsa leads up a global next-generation K-12 learning solutions provider that recently acquired Outlier, an edtech startup that has created a portfolio of high-quality online college-level courses enabling high school students to earn dual credit while never having to leave their school building. A leader whose career in education and publishing spans over two decades, Bethlam has guided Savvas to deliver award-winning product lines. The Savvas Realize digital platform won the 2024 EdTech Digest Cool Tool Award for Learning Management System Solution. The company’s enVision Mathematics and SuccessMaker: Foundations of High School Math were named EdTech Cool Tool Award finalists in the Math Solution and the Personalized Learning Solution categories, respectively. Bethlam was also recently named the Most Influential Thought Leader in EdTech by the 2024 CODiE Awards. In this EdTech Digest exclusive, hear why she first became drawn to the work of education, how she defines an oft-used phrase, and her thoughts on technology’s role in—and what the future of—learning might look like.
What prompted you to first become involved with technology and learning?
I was drawn to this work because I fundamentally believe in the power of education to shape lives. The best way to make a difference in the world is through a quality education. To do this, all of us at Savvas have dedicated ourselves to supporting educators by developing innovative, high-quality learning solutions that enable all students to succeed. Early in my career, I realized that education technology, when leveraged effectively, could revolutionize the way students learn, just as it has empowered us in our everyday lives. We’ve long been a leader in the digital transformation of K-12 education, including our pioneering use of adaptive technology to provide personalized learning solutions that help educators meet the needs of all learners. Our learning management system, Savvas Realize, — it’s earned more than a dozen edtech innovation awards, including one from EdTech Digest — has been widely recognized as a game-changing platform known for driving innovation and exemplifying the best in edtech solutions. It’s an exciting time to be in the edtech industry. Technology will continue to significantly impact education, especially with the advent of generative AI and the possibility of it taking personalized learning to new heights.
‘It’s an exciting time to be in the edtech industry. Technology will continue to significantly impact education, especially with the advent of generative AI and the possibility of it taking personalized learning to new heights.’
How do you define “next-generation learning,” and why?
There is a simple truth in education that student engagement leads to student achievement. For us, next-generation learning is about combining the power of advanced technology with research-based pedagogy and compelling content to deliver interactive, real-world learning experiences that spark student engagement and drive student achievement. Another important aspect of next-generation learning is enabling differentiated instruction to meet the needs, skills, and interests of individual learners, making education personalized, relevant and engaging to each student. We know that the traditional, one-size-fits-all approach doesn’t work for all learners. That is why at Savvas we make it our mission to design flexible learning solutions that enable teachers to tailor instruction to each student’s distinct learning style and pace, ensuring that they receive personalized support and challenges that align with their abilities and goals. The educators and students who use our learning solutions deserve nothing less.
What does “proven pedagogy” mean to you and the team behind Savvas?
We take great pride in developing the highest-quality instructional materials available in today’s educational marketplace. Proven pedagogy is the very foundation of this. What it means to me, and all of us at Savvas, is that we develop research-based, standards-aligned learning solutions that incorporate the most current educational best practices coupled with compelling, relevant, and accurate content — all developed by dedicated teams of authors who are experts in their fields, working in conjunction with top editors, academic consultants, and teacher reviewers. In order to create our high-quality, evidence-based curriculum, we adhere to strict editorial standards and a rigorous product development process.
‘In order to create our high-quality, evidence-based curriculum, we adhere to strict editorial standards and a rigorous product development process.’
In the end, the gold standard for us is ensuring that the educational solutions we provide our customers prove to be efficacious and improve the educational outcomes and opportunities for all learners. As such, we believe that rigorous research should include multiple studies, creating a large body of research supporting an educational solution. For us, this involves continuously conducting research to measure the effectiveness of a product, as well as gain insights into educators’ experiences in the classroom. We partner with educators and school districts nationwide to constantly evaluate and test our instructional materials and drive development of evidence-based learning solutions. Our extensive research, combined with the feedback we receive from educators and teachers who use our programs, helps inform every step of our product development process, from pedagogy and instructional design to usability and efficacy in the classroom. Our goal is to ensure we deliver the most effective learning solutions that make a positive impact so that every student — no matter where they come from or which school they are in — has an opportunity to achieve their full potential.
What are some important guiding elements involved in helping students become productive contributing adults?
I believe it is important that we impart in young students the skills to help them thrive not just in the classroom but in life. Critical thinking, problem-solving, collaboration, teamwork, and other soft skills are essential for preparing students to navigate the complexities of adulthood. Likewise, encouraging curiosity and a love for lifelong learning can equip students with the mindset they’ll need to continuously adapt and grow.
As a next-generation K-12 education leader, Savvas develops instructional materials that make learning relevant to students’ lives and prepare them for college and career. Our programs challenge students to think more deeply and analytically about what they’ve read, giving them the tools to become critical thinkers and effective communicators, which is especially important in today’s fast-paced digital world. I think it’s important to also provide educators solutions that build upon the strong foundation of knowledge and life skills that they’ve taught their students by delivering personalized pathways to support college and career readiness for their high schoolers. Giving students the ability to “try on” college and earn valuable credit through dual-enrollment courses or take career technical education (CTE) classes to launch their careers is a critical next step to helping high school students become productive adults.
‘Giving students the ability to “try on” college and earn valuable credit through dual-enrollment courses or take career technical education (CTE) classes to launch their careers is a critical next step to helping high school students become productive adults.’
What trends are you looking at (AI, others) with the ‘future of learning’ in mind? And what does that look like to you—what do you see in the next couple of years for Savvas, for learning generally?
We are actively developing ways to use generative AI to create time-saving tools for teachers to reduce their burdensome administrative tasks, such as grading assignments and lesson planning. This will help make their jobs easier and alleviate some of the causes that have led to teacher burnout, allowing them to do what they love most: instructing students.
We are also leveraging AI to generate even more robust data-driven insights to differentiate instruction and enhance the adaptivity of our learning solutions. Another major focus of ours is using AI-powered tools to develop highly sophisticated and, most importantly, reliable “tutorbots” or “coaches” that can provide real-time feedback to students to improve their writing and math skills. The idea is that we want students to get better at literacy and math, and with machine-learning capabilities we can now give them in-the-moment, constructive critiques of their work that can guide them to become stronger at writing and math.
These are just some of the ways we are incorporating AI into our learning solutions. The use of generative AI in K-12 education is going to grow rapidly as more use cases are identified. Looking ahead, I think generative AI offers the potential to provide a deeply personalized learning experience like we’ve never seen before. It will bring to market new solutions to solve real world problems for teachers and learners in ways that were not previously possible. However, like with any new technology, the use of AI’s capabilities as a classroom tool must be pedagogically sound and implemented responsibly, with clear guardrails for its use that prioritize safety, integrity, and efficacy, above all else. Lastly, we must ensure that we keep teachers and students at the center of whatever learning solutions we create. AI may help revolutionize learning but it will never replace the teacher.
Anything you care to add or emphasize concerning edtech, the future of learning, or anything else regarding tech’s role in learning?
Edtech will continue to have a significant role in the future of learning. There’s no doubt about that. However, what I think is really important to point out is that edtech is also now reimagining learning for the future.
‘Edtech will continue to have a significant role in the future of learning. There’s no doubt about that. However, what I think is really important to point out is that edtech is also now reimagining learning for the future.’
We all know it’s vitally important that we prepare today’s students with the skills they will need to be successful in college and the workforce. According to the U.S. Department of Education, 70 percent of jobs will soon require education or training beyond high school. Yet only two in 10 high school students believe they are career-ready, according to a 2021 survey by the Ewing Marion Kauffman Foundation.
Dual-enrollment courses can help fill the gap in college and career readiness by allowing students to simultaneously earn course credit for both high school and college while also exploring career pathways and learning skills needed in the job market. However, logistical challenges in the way these courses have been traditionally offered have often posed barriers to students. For example, in many cases students have long needed to travel to a nearby college — if one even exists in their community — to take a college-level dual-enrollment course.
By utilizing technology, we can eliminate those barriers. Through our acquisition of Outlier, we are now able to offer the millions of students we serve the opportunity to experience the rigor of college courses through high-quality, online dual enrollment courses. Credit for these courses come from the University of Pittsburgh, a top 50 school, and are highly transferable. Since Outlier’s cinematically produced courses are offered asynchronously, students can take them virtually — in the comfort of their own school, at a time that fits conveniently into their high school schedule. What’s really exciting about these state-of-the-art Outlier offerings is that students, no matter where they live, can experience college-level courses taught by professors from Harvard, MIT, NYU and other first-rate institutions without the hassle of leaving their building or missing other classes. There’s nothing else like it on the market that makes earning college credit while in high school so accessible for students, jump-starting learning for their future. Outlier technology is the future of learning, happening now.
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Victor Rivero is the Editor-in-Chief of EdTech Digest. Write to: victor@edtechdigest.com
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This is a comprehensive Learning Management System (LMS) that enhances the educational experience. The digital platform connects educators, students, and parents to facilitate interactive and engaging learning. It provides an app and webpage user-friendly interface for educators to efficiently create, manage, and deliver educational content. Administrators can effortlessly set up classes and curriculums, add users, and assign content, whether it is custom or pre-existing. Students can access this content, complete assignments, and track their progress. Students and teachers can use a stylus on the app to write directly on the assignment. It also has a helpful writing feature to display work. Part of its real value is actually quite basic: it allows efficient communication between teachers, students, and parents.
EdQuill was developed by a team with Ushapriya Ravilla to address the evolving needs of modern education. EdQuill aims to improve the teaching and learning experience, reduce administrative burdens on educators, and foster greater parent involvement in students’ education. It is available to educational institutions, learning centers, tutors, and educators. Access EdQuill by signing up for a free trial or scheduling a demo with EdQuill’s expert representatives.
In two short years, EdQuill has helped over 100 educators impact more than 2,000 students nationwide. Using the platform led to a 40% decrease in printing costs and increased productivity in administrative tasks for 100% of teachers. For these reasons and more, EdQuill earned a Cool Tool Award (finalist) for “Best Classroom Management Solution” as part of The EdTech Awards 2024 from EdTech Digest. Learn more.
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Archipel Academy was born as the Managed Learning Services business line of Schouten & Nelissen. As learning consultants, the people behind it become very familiar with the challenges in the Learning & Development community and in 2019 their founder and CEO Omar Fouab decided to make learning more personal, more impactful and more accessible.
That is how their Learning Management System with LXP capabilities (Learning tracks and skill management) and the biggest content marketplace in Europe was born. Since then, they kept adding features to keep their customers as well as employees everywhere fit for future: the “search & book” function is AI-driven, learners can access an AI-powered job coach that helps them further develop in their current role or grow towards a different position, engagement is kept high through the use of gamification and more recently they added in-real-time learning through the Archi chatbot.
All of these developments have at their core the company’s strong motivation to keep employees and organizations what they call “fit for future.”
Since becoming their customer, VodafoneZiggo has managed to achieve the following numbers:
• Employees followed an average of 2.3 trainings/ year/ per person, which is 4x more than before
• 31% decrease in voluntary resignation
• In 2022 savings of 1.5 milion euros on learning costs, excluding the 5 administrative FTEs that were repurposed to focus on high-level people strategy
• Becoming an employer of choice- with 1 in 3 people applying at VodafoneZiggo doing so because of the learning culture
• Filling 60% of vacancies from within and adressing knowledge gaps through learning
For these reasons and more, Archipel Academy was named “Best All-in-One Learning Platform” as part of The EdTech Awards 2024 from EdTech Digest. Learn more.
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Synergy Student Information System empowers districts to do more, saving time and money while helping to improve efficiency and educational outcomes. Some of the benefits of this system include:
Deep Functionality – All the data and process management functionality districts expect from a world-class SIS, extending beyond traditional SIS boundaries to deliver greater value.
TeacherVUE Portal – Powerful classroom management and communication tools that make everyday tasks faster and easier for teachers, along with a powerful gradebook.
Exceptional Data Access for Reporting and Analysis – Robust tools for reporting, analyzing data, identifying issues and trends, and ensuring that stakeholders get the information they need to solve problems and support student learning.
Highly Configurable – Extensive configuration options out of the box, with Synergy Technology Development Toolkit available to be licensed by districts along with Synergy source code for rapidly developing custom applications that are fully integrated to the SIS.
Data Security – The highest level of privacy and security in compliance with FERPA and HIPAA requirements, with full field-level security systemwide, and Edupoint is a signatory of the Student Privacy Pledge.
Custom Data Validation and Rules Engine – Safeguards to preserve data integrity and eliminate redundancies platformwide.
Easy to Use – Intuitive and easy to use and personalize from day one – even for beginners – reducing training costs and minimizing inefficiency while users get up to speed.
Edupoint is focused on K-12 student data management and boasts a 99.5% renewal rate. For these reasons and more, Synergy from Edupoint Educational Systems is a Cool Tool Award Winner for “Best Student Information System Solution” as part of The EdTech Awards 2024 from EdTech Digest. Learn more.
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