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July 14, 2025
When was the last time you got a new phone? For me, it was last week. Before that, it might have been three weeks ago. Before that, well, you get the idea — I have some of the best Android phones from the last few years floating around my apartment. In some cases, I have multiple generations of the same lineup, like Google Pixel devices back to the Pixel 6 series and iPhones back to the iPhone 12, and I’ve used them all. It’s a fortunate position to be in, but also one that shows how little tends to change from one year to the next. As someone whose SIM is always on the move, here’s how often I think you should get a new phone.
First and foremost, you don’t need a new phone every year. I certainly wouldn’t get one that often if I weren’t in my position. If I’ve learned one thing from my constant stream of new phones, the more they claim to change, the more they tend to stay the same. Apple, Google, and Samsung can mention everything new over the course of an hour-long keynote address, but by the time we get the latest device in hand, it feels so similar to the previous generation. Having reviewed them all, the iPhone 16 feels a lot like the iPhone 14 and 15 did before it, and other than minimizing their camera cutouts, the last few generations of Galaxy S flagships have all felt — and looked — identical.
That said, sometimes a significant change will come along that makes you wonder if it’s time to update. Sometimes it is. Apple ushered in its Dynamic Island on the iPhone 14 Pro series, and Google debuted its Tensor chip and revamped camera bar on the Pixel 6 series, both of which felt like steps away from established designs. The Pixel 6 Pro remains the last phone I bought with my own money, and I remember being so excited to see Google finally break free from an endless stream of single-color devices and corner-mounted camera bumps. It was finally an innovation that brought more of the Pixel process in-house and started a leadup to longer software support and a more seamless Pixel experience.
Since then, things have slowed down — and not just for the Pixel. The Tensor chip has improved in its G2, G3, and G4 generations, and Google has swapped a few other bits of its Pixel design, but none of it — at least from a hardware perspective — has added up to make some people ready to spend $1,000 on a new phone. Google’s chipset still runs warm at times, its older devices are perfectly capable of image processing, and it’s still tough to top some of the original Pixel 6 color options, even though I think I bought mine in Stormy Black anyway.
To an extent, even foldable phones have been lured into a cycle of minor updates. Both Samsung and Motorola adopted larger cover displays for their flip phones in 2023 (which they then kept in 2024), but before that, the Galaxy Z Flip and Razr were starting to feel like rinse-and-repeat launches. They had similar cameras and almost the same hinges for a few years, and choosing to update would cost you a cool $1,000 for a phone that might not last its entire update cycle.
And then, 2025 happened. Both Samsung and Motorola decided to give their flagship flip phones a heap of love, with the former finally expanding its Flex Window on the Galaxy Z Flip 7 and introducing a slightly more affordable Fan Edition model, and the latter debuting the no-holds-barred Razr Ultra. The two brand-new flip phones finally feel like exactly what we’ve been looking for when we’ve exercised patience and caution before buying a new phone.
Mentioning updates, that’s probably the main reason I’d tell someone to buy a new phone right now. Not for the current state of Android or iOS itself — they haven’t changed much in a few years — but for the fact that you can finally expect more than three or four years of continued updates. After all, if you spend the money on a Pro-level iPhone or anything with Ultra in the name, it’s better to know that you have seven years of support ahead of you rather than half of that.
Yet, even with an improved update timeline, I find myself willing to recommend previous-generation flagships more and more as I write reviews. They tend to pack similar cameras and carry comparable displays, and they can often be had at a much more comfortable price. Sure, you’ve probably already missed out on a year of updates, but they’ll be available when you set up your phone, and the money you can save is more than worth your while.
There is, however, one thing that might have you eyeing up an upgrade — AI. Now, on-device AI isn’t a must-have feature just yet, but given its strict hardware requirements, it will probably push you into a new phone sooner rather than later. Google has reserved most of its Gemini Nano experience for the Pixel 8 and Pixel 9 series, Samsung’s Galaxy AI is only fully available on the traditional Galaxy S and foldable Galaxy Z lineups, and Apple Intelligence has finally arrived on the iPhone 15 Pro duo and iPhone 16 series.
But, as I said, on-device AI isn’t as important as a reliable set of cameras or a vibrant, smooth display — at least not yet. Instead, it feels like Google’s first crack at an in-house chipset — flawed, but a step in the right direction. Where we couldn’t trust the original Tensor to stay cool under pressure, we can’t always trust the accuracy of generative AI features as they land. Generated wallpapers are still full of wonky artifacts, and text often feels robotic and artificial, but both have come a long way in their relatively short histories. They just still have a way to go.
Don’t get me wrong, there are some AI-powered features that I’ve started to embrace. Circle to Search is a noticeable improvement on Google Lens, and Magic Editor is a logical step beyond Magic Eraser, but other features like Google’s Magic Compose feel slightly sillier. I can count on zero hands the number of times I’ve wanted to rephrase a text message to sound like Shakespeare before sending it to a friend.
To put it another way, AI on smartphones currently feels like the early generations of foldable phones. Sure, it does some neat tricks, and it’ll certainly catch the eye of your nerdier friends, but it’s not quite ready for everyone. Honestly, foldable phones still aren’t ready for everyone, given their lower durability and smaller batteries, and they’ve had generations to iron out the kinks — er, creases. Honestly, I have AI-capable devices in the Pixel 9 Pro and Galaxy S25 Ultra, but I don’t find myself using them any differently from my standard iPhone 15.
Now, for the potentially more than $1,000 question — when should you update your phone? Well, the simple answer would be when it stops working. If something happens to it that will cost more to fix than the phone is worth, like a shattered display or a busted battery, it’s time to upgrade. If you’ve run out of Android (or iOS) updates and security patches, it’s time to upgrade. Otherwise, you might want to take a long, hard look at your current phone before you relegate it to the great tech cloud above (or sell it to someone else). When you decide to upgrade, you’ll probably have an all-new slate of the best camera phones and some of the most powerful hardware to choose from.
Since phones are guaranteed additional years of updates, are slightly easier to repair, and — let’s face it — have reached something of a plateau, there’s less reason to run out for the latest iPhone or Pixel. Who will tease you for not having the latest and greatest phone when it looks almost the same as its predecessor from two or three years ago? I’d rather have money in my pocket and a phone that does everything I need than the street cred associated with whipping out the Samsung Galaxy Z Scroll Super Max Ultra (this isn’t an actual phone, but it might have made Unpacked more exciting).
And yes, I know that telling you not to buy a new phone goes directly against what I do for a living. I know that I should be pressing you to upgrade your phone using one of our affiliate links so that we can get some money and keep the lights on (which would be nice). But I also know that phones and accessories like cases and earbuds are extremely expensive. Life is expensive, too. Unless you can write 1,500 words — approximately the length of this piece — on why a new phone would make your life better than your current, still-supported phone, you can probably wait a little longer.

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Pixel 6a owners may soon see their devices limited in terms of battery life, as code shows that Google is preparing to “reduce” the battery to avoid “a potential overheating” issue.
Earlier this year, Google rolled out an update to Pixel 4a that effectively made device useless. Battery life was cut to the point where the device needed to be charged after as little as a couple of hours. Google offered battery replacements to many, but the issue at hand was never fully confirmed, though it was seemingly to avoid an overheating issue that could leave devices catching fire.
Now, we’re getting a repeat as Google is apparently preparing similar measures for 2022’s Pixel 6a, right as reports of devices catching fire have been circulating.
Hidden within Android 16 QPR1 Beta 2, some lines of code suggest that Google will “reduce” the battery capacity of Pixel 6a via an upcoming software update and will recommend a battery replacement to users of the device. The code, as spotted by Android Authority, says:
Due to a potential battery overheating issue, battery capacity and charging performance are reduced, and battery replacement is recommended. Support options are available, visit g.co/pixel/6abattery for more information.
In another snippet, Google specifies that this new reduction to the Pixel 6a’s battery will take place after the device has reached 400 charge cycles, with a warning notification being shown after 375 cycles.
Your battery has reached 375 charge cycles. Due to a potential battery overheating issue, when your battery reaches 400 charge cycles the battery capacity and charging performance will be reduced and battery replacement is recommended. Support options are available, visit g.co/pixel/6abattery for more information.
The mentioned support page is not yet live, but it does seem as though this risk is already quite real.
Reports of Pixel 6a phones are limited thus far, but incidents aren’t exactly isolated either. In early May, one user noted that their device “caught fire in the middle of the night” while charging using a third-party charger. Another Pixel 6a user reported the same behavior just earlier this month on the same thread. There are two other examples of this on Reddit, one from last week and another from a few months ago, and one user sent images of their Pixel 6a that caught fire to Android Authority last month with their experience dealing with Google support which ended with a replacement device.
Google has yet to publicly confirm this update, as well as support options available to Pixel 6a owners, but anyone still using these devices should certainly be cautious. It’s unclear if Google will wait until the QPR1 release in the coming months to make this change, or if it will kick in earlier than that.
Update: In a statement shared with 9to5Google, Google says this update is coming and will be mandatory.
A subset of Pixel 6a phones will require a mandatory software update to reduce the risk of potential battery overheating. The update will enable battery management features that will reduce capacity and charging performance after the battery reaches 400 charge cycles. We’ll contact impacted customers next month, with all the information they need to address the issue.
Images via u/zaliver on Reddit and Android Authority
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AWS Blogs
TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. TUI Group covers the end-to-end tourism chain with over 400 owned hotels, 16 cruise ships, 1,200 travel agencies, and 5 airlines covering all major holiday destinations around the globe. At TUI, crafting high-quality content is a crucial component of its promotional strategy.
The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. This content needs to adhere to TUI’s tone of voice, which is essential to communicating the brand’s distinct personality. But as its portfolio expands with more hotels and offerings, scaling content creation has proven challenging. This presents an opportunity to augment and automate the existing content creation process using generative AI.
In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost.
Through experimentation, we found that following a two-phased approach worked best to make sure that the output aligned to TUI’s tone of voice requirements. The first phase was to fine-tune with a smaller large language model (LLM) on a large corpus of data. The second phase used a different LLM model for post-processing. Through fine-tuning, we generate content that mimics the TUI brand voice using static data and which could not be captured through prompt engineering. Employing a second model with few-shot examples helped verify the output adhered to specific formatting and grammatical rules. The latter uses a more dynamic dataset, which we can use to adjust the output quickly in the future for different brand requirements. Overall, this approach resulted in higher quality content and allowed TUI to improve content quality at a higher velocity.
The architecture consists of a few key components:
The following diagram is the high-level architecture of the solution.

We started by crafting prompts that include the most common issues identified by the TUI content team, including tone of voice, accuracy, length, and grammar. We provided the LLM with a few examples of curated content within the context window. Although the generated output followed the guidance, the writing style didn’t meet TUI’s tone of voice requirements.
 Example prompt:
Here is an example of the results we received.
To evaluate these results, we generated a dataset of 150 hotel descriptions using two different prompt versions and asked our business teams to review it. However, they flagged 98% of the output with significant issues. Firstly, Anthropic’s Claude 2 struggled to adhere to TUI’s tone of voice and branding guidelines, frequently employed abbreviations, and favored American English over British English. It also failed to follow the SEO guidelines provided and sometimes generated false information regarding hotel facilities and locations. The following image shows a list of these challenges and how the LLM handled them. Of the six challenges, the LLM met only one.

These issues and poor feedback led us to conclude that prompt engineering alone would not adequately address the problem. As a result, we decided to pursue an alternative approach: fine-tuning a smaller large language model to rewrite the text in accordance with TUI’s tone of voice. We used a curated set of hotel descriptions written by TUI copywriters so that the model would have better alignment with our guidelines.
We selected the Meta Llama 2 model using Amazon SageMaker JumpStart, one of the top open source LLMs available at the time, and chose the 13B parameter version to apply parameter-efficient fine-tuning (PEFT), specifically using quantized low-rank adaptation (QLoRA). This technique quantizes the pre-trained model to 4 bits and adds small low-rank adapters for fine-tuning. We fine-tuned the model on a single ml.g5.4xlarge instance in about 20 hours using a relatively small dataset of around 4,500 hotels. We also tested out the Llama 2 7B and 70B models. We found that the 7B model didn’t perform well enough, and the 70B model had much higher costs without seeing significant improvement.
We used common natural language processing (NLP) evaluation metrics, such as perplexity for evaluation and monitoring during training, and established daily feedback loops with the content team to refine the test set. The output from the fine-tuned Meta Llama 2 13B model effectively mimicked the TUI tone of voice and adhered to most SEO guidelines, even those not specified in the prompt.
For more information, refer to Fine-tune Llama 2 for text generation on Amazon SageMaker Jumpstart.
Make yourself at home in Munich at the München City Süd. This former NH München am Ring sits just southeast of the Bavarian capital, putting both the highway to Salzburg and the airport within easy reach. The S-Bahn station Karl-Preis-Platz is a quick walk away too, so you can be in the city center in just 20 minutes.
The neighborhood here is peaceful, giving you the chance to properly relax during your stay. The hotel’s 163 rooms are decorated in calming white and red hues. Free Wi-Fi and laptop-sized safes come as standard, while spacious desks make working easy. For longer visits, apartments with separate living areas are available. The suites take things up a notch with their own living spaces and foldaway beds on request.
Fuel up each morning with a vast breakfast spread of cold cuts, cheeses, pastries and fruit. All washed down with coffee, tea and fresh juices. You’ll find many dining options close by for other meals. After a busy day, unwind in the sauna or park up in the garage.
The following image shows a list of the challenges and how the LLM handled them. Of the six challenges, the LLM met four.

To further improve the output, we introduced Anthropic’s Claude 2 using Amazon Bedrock as a final refinement step. This included converting American spelling to British spelling, writing numbers one through nine in words and larger numbers in digits, correcting typos and capitalization errors, minimizing banned words, incorporating essential TUI branding words, and adding missing hotel information. We also implemented a feedback mechanism in the UI to use data for ongoing fine-tuning in production. By using Anthropic Claude 2, we make sure that the final output applies the remaining formatting rules.
As its name suggests, Vienna Sporthotel has loads lined up for sports fans.
There are 10 tennis courts and four golf simulators here, plus, you can book a session with a coach to brush up on your skills. On top of that, there’s an outdoor pool, a well-stocked fitness room and a big sauna. To fuel up for all that exercise, you can grab a plate at the breakfast buffet.
Location-wise, this place is set up for sightseeing. Hop on a bus and you can reach the likes of The Hofburg, Belvedere Palace and Karlskirche in less than half an hour. You’re only a 10-minute stroll away from the nearest restaurants and cafes, too.
With a name like Vienna Sporthotel, you can expect this place to serve up plenty for fans of sport and activity. Brush up your skills on one of 10 tennis courts or in the four golf simulators, then recharge in the outdoor pool or well-equipped gym. There’s even a coach on hand to help perfect your technique.
When you’re ready to refuel, tuck into the breakfast buffet. Then get set for sightseeing – a bus ride whisks you to top Vienna attractions like The Hofburg, Belvedere Palace and Karlskirche in under 30 minutes. You’re also just a short stroll from local eateries and coffee shops.
The following image shows a list of the challenges and how the LLM handled them. The LLM met all six challenges.

The final architecture consists of a fine-tuned Meta Llama 2 13B model and Anthropic Claude 2, using the strengths of each model. In a blind test, these dynamically generated hotel descriptions were rated higher than those written by humans in 75% of a sample of 50 hotels. We also integrated a third-party API to calculate SEO scores for the generated content, and we observed up to 4% uplift in SEO scores for the generated content compared to human written descriptions. Most significantly, the content generation process is now five times faster, enhancing our team’s productivity without compromising quality or consistency. We can generate a vast number of hotel descriptions in just a few hours— a task that previously took months.
Moving forward, we plan to explore how this technology can address current inefficiencies and quality gaps, especially for hotels that our team hasn’t had the capacity to curate. We plan to expand this solution to more brands and regions within the TUI portfolio, including producing content in various languages and tailoring it to meet the specific needs of different audiences.
Throughout this project, we learned a few valuable lessons:
Although Meta Llama 2 and Anthropic’s Claude 2 were the latest state-of-the-art models available at the time of our experiment, since then we have seen the launch of Meta Llama 3 and Anthropic’s Claude 3.5, which we expect can significantly improve the quality of our outputs. Amazon Bedrock also now supports fine-tuning for Meta Llama 2, Cohere Command Light, and Amazon Titan models, making it simpler and faster to test models without managing infrastructure.
Nikolaos Zavitsanos is a Data Scientist at TUI, specialized in developing customer-facing Generative AI applications using AWS services. With a strong background in Computer Science and Artificial Intelligence, he leverages advanced technologies to enhance user experiences and drive innovation. Outside of work, Nikolaos plays water polo and is competing at a national level. Connect with Nikolaos on Linkedin
Hin Yee Liu is a Senior Prototyping Engagement Manager at Amazon Web Services. She helps AWS customers to bring their big ideas to life and accelerate the adoption of emerging technologies. Hin Yee works closely with customer stakeholders to identify, shape and deliver impactful use cases leveraging Generative AI, AI/ML, Big Data, and Serverless technologies using agile methodologies. In her free time, she enjoys knitting, travelling and strength training. Connect with Hin Yee on LinkedIn.
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AWS Blogs
EBSCOlearning offers corporate learning and educational and career development products and services for businesses, educational institutions, and workforce development organizations. As a division of EBSCO Information Services, EBSCOlearning is committed to enhancing professional development and educational skills.
In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the power of generative AI in revolutionizing their learning assessment process. We explore the challenges faced in traditional question-answer (QA) generation and the innovative AI-driven solution developed to address them.
In the rapidly evolving landscape of education and professional development, the ability to effectively assess learners’ understanding of content is crucial. EBSCOlearning, a leader in the realm of online learning, recognized this need and embarked on an ambitious journey to transform their assessment creation process using cutting-edge generative AI technology.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI, and is well positioned to address these types of tasks.
EBSCOlearning’s learning paths—comprising videos, book summaries, and articles—form the backbone of a multitude of educational and professional development programs. However, the company faced a significant hurdle: creating high-quality, multiple-choice questions for these learning paths was a time-consuming and resource-intensive process.
Traditionally, subject matter experts (SMEs) would meticulously craft each question set to be relevant, accurate, and to align with learning objectives. Although this approach guaranteed quality, it was slow, expensive, and difficult to scale. As EBSCOlearning’s content library continues to grow, so does the need for a more efficient solution.
Recognizing the potential of AI to address this challenge, EBSCOlearning partnered with the GenAIIC to develop an AI-powered question generation system. The goal was ambitious: to create an automated solution that could produce high-quality, multiple-choice questions at scale, while adhering to strict guidelines on bias, safety, relevance, style, tone, meaningfulness, clarity, and diversity, equity, and inclusion (DEI). The QA pairs had to be grounded in the learning content and test different levels of understanding, such as recall, comprehension, and application of knowledge. Additionally, explanations were needed to justify why an answer was correct or incorrect.
The team faced several key challenges:
The GenAIIC team developed a sophisticated pipeline using the power of large language models (LLMs), specifically Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock. This pipeline is illustrated in the following figure and consists of several key components: QA generation, multifaceted evaluation, and intelligent revision.

The process begins with the QA generation component. This module takes in the learning content—which could be a video transcript, book summary, or article—and generates an initial set of multiple-choice questions using in-context learning.
EBSCOlearning experts and GenAIIC scientists worked together to develop a sophisticated prompt engineering approach using Anthropic’s Claude 3.5 Sonnet model in Amazon Bedrock. To align with EBSCOlearning’s high standards, the prompt includes:
The system aims to generate up to seven questions for each piece of content, each with four answer choices including a correct answer, and detailed explanations for why each answer is correct or incorrect.
After the initial set of questions is generated, it undergoes a rigorous evaluation process. This multifaceted approach makes sure that the questions adhere to all quality standards and guidelines. The evaluation process includes three phases: LLM-based guideline evaluation, rule-based checks, and a final evaluation.
In collaboration with EBSCOlearning, the GenAIIC team manually developed a comprehensive set of evaluation guidelines covering fundamental requirements for multiple-choice questions, such as validity, accuracy, and relevance. Additionally, they incorporated EBSCOlearning’s specific standards for diversity, equity, inclusion, and belonging (DEIB), in addition to style and tone preferences. The AI system evaluates each question according to the established guidelines and generates a structured output that includes detailed reasoning along with a rating on a three-point scale, where 1 indicates invalid, 2 indicates partially valid, and 3 indicates valid. This rating is later used for revising the questions.
This process presented several significant challenges. The primary challenge was making sure that the AI model could effectively assess multiple complex guidelines simultaneously without overlooking any crucial aspects. This was particularly difficult because the evaluation needed to consider so many different factors—all while maintaining consistency across questions.
To overcome the challenge of LLMs potentially overlooking guidelines when presented with them all at one time, the evaluation process was split into smaller manageable tasks by getting the AI model to focus on fewer guidelines at a time or evaluating smaller chunks of questions in parallel. This way, each guideline receives focused attention, resulting in a more accurate and comprehensive evaluation. Additionally, the system was designed with modularity in mind, streamlining the addition or removal of guidelines. Because of this flexibility, the evaluation process can adapt quickly to new requirements or changes in educational standards.
By generating detailed, structured feedback for each question, including numerical ratings, concise summaries, and in-depth reasoning, the system provides invaluable insights for continual improvement. This level of detail allows for a nuanced understanding of how well each question aligns with the established criteria, offering possibilities for targeted enhancements to the question generation process.
Some quantitative aspects of question quality proved challenging for the AI to consistently evaluate. For instance, the team noticed that correct answers were often longer than incorrect ones, making them straightforward to identify. To address this, they developed a custom algorithm that analyzes answer lengths and flags potential issues without relying on the LLM’s judgment.
Beyond evaluating individual questions, the system also assesses the entire set of questions for a given piece of content. This step checks for duplicates, promotes diversity in the types of questions asked, and verifies that the set as a whole provides a comprehensive assessment of the learning material.
One key component of the pipeline is the intelligent revision module. This is where the iterative improvement happens. When the evaluation process flags issues with a question—whether it’s a guideline violation or a structural problem—the question is sent back for revision. The AI model is provided with specific feedback on how it can address the specific violation and directs it to fix the issue by revising or replacing the QA.
The whole pipeline goes through multiple iterations until the question aligns with all of the specified quality standards. If after several attempts a question still doesn’t meet the criteria, it’s flagged for human review. This iterative approach makes sure that the final output isn’t merely a raw AI generation, but a refined product that has gone through multiple checks and improvements.
Throughout the development process, the team maintained a strong focus on iterative tracking of changes. They implemented a unique history tracking system so they could monitor the evolution of each question through multiple rounds of generation, evaluation, and revision. This approach not only provided valuable insights into the AI model’s decision-making process, but also allowed for targeted improvements to the system over time. By closely tracking the AI model’s performance across multiple iterations, we were able to fine-tune our prompts and evaluation criteria, resulting in a significant improvement in output quality.
With EBSCOlearning’s vast content library in mind, the team built scalability into the core of their solution. They implemented multithreading capabilities, allowing the system to process multiple pieces of content simultaneously. They also developed sophisticated retry mechanisms to handle potential API failures or invalid outputs so the system remained reliable even when processing large volumes of content.
By combining these components—intelligent generation, comprehensive evaluation, and adaptive revision—EBSCOlearning and the GenAIIC team created a system that not only automates the process of creating assessment questions but does so with a level of quality that rivals human-created content. This pipeline represents a significant leap forward in the application of AI to educational content creation, promising to revolutionize how learning assessments are developed and delivered.
The impact of this AI-powered solution on EBSCOlearning’s assessment creation process has been nothing short of transformative. Feedback from EBSCOlearning’s subject matter experts has been overwhelmingly positive, with the AI-generated questions meeting or exceeding the quality of manually created ones in many cases.
Key benefits of the new system include:
“The Generative AI Innovation Center’s automated solution for generating multiple-choice questions and answers considerably accelerated the timeline for deployment of assessments for our online learning platform. Their approach of leveraging advanced language models and implementing carefully constructed guidelines in collaboration with our subject matter experts and product management team has resulted in assessment material that is accurate, relevant, and of high quality. This solution is saving us considerable time and effort, and will enable us to scale assessments across the wide range of skills development resources on our platform.”
—Michael Laddin, Senior Vice President & General Manager, EBSCOlearning.
Here are two examples of generated QA.
Question 1: What does the Consumer Relevancy model proposed by Crawford and Mathews assert about human values in business transactions?
Correct answer: C
Answer explanations:
Overall: The Book Summary states that the Consumer Relevancy model asserts that human values are more important than traditional value propositions and that businesses must recognize this need for human values as the contemporary currency of commerce.
Question 2: According to Sara N. King, David G. Altman, and Robert J. Lee, what is the primary benefit of leaders having clarity about their own values?
Correct answer: B
Answer explanations:
Overall: The Book Summary emphasizes that having clarity about one’s own values allows leaders to make more fulfilling career choices and helps them recognize when they are participating in actions that conflict with their core values.
For EBSCOlearning, this project is the first step towards their goal of scaling assessments across their entire online learning platform. They’re already planning to expand the system’s capabilities, including:
The potential applications of this technology extend far beyond EBSCOlearning’s current use case. From personalized learning paths to adaptive testing, the possibilities for AI in education and professional development are vast and exciting.
The collaboration between EBSCOlearning and the GenAIIC demonstrates the transformative power of AI when applied thoughtfully to real-world challenges. By combining cutting-edge technology with deep domain expertise, they’ve created a solution that not only solves a pressing business need but also has the potential to enhance learning experiences for millions of people. This solution is slated to produce assessment questions for hundreds and eventually thousands of learning paths in EBSCOlearning’s curriculum.
As we look to the future of education and professional development, it’s clear that AI will play an increasingly important role. The success of this project serves as a compelling example of how AI can be used to create more efficient, effective, and engaging learning experiences.
For businesses and educational institutions alike, the message is clear: embracing AI isn’t just about keeping up with technology trends—it’s about unlocking new possibilities to better serve learners and drive innovation in education. As EBSCOlearning’s journey shows, the future of learning assessment is here, and it’s powered by AI. Consider how such a solution can enrich your own e-learning content and delight your customers with high quality and on-point assessments. To get started, contact your AWS account manager. If you don’t have an AWS account manager, contact sales. Visit Generative AI Innovation Center to learn more about our program.
Yasin Khatami is a Senior Applied Scientist at the Generative AI Innovation Center. With more than a decade of experience in artificial intelligence (AI), he implements state-of-the-art AI products for AWS customers to drive innovation, efficiency and value for customer platforms. His expertise is in generative AI, large language models (LLM), multi-agent techniques, and multimodal learning.
Yifu Hu is an Applied Scientist at the Generative AI Innovation Center. He develops machine learning and generative AI solutions for diverse customer challenges across various industries. Yifu specializes in creative problem-solving, with expertise in AI/ML technologies, particularly in applications of large language models and AI agents.
Aude Genevay is a Senior Applied Scientist at the Generative AI Innovation Center, where she helps customers tackle critical business challenges and create value using generative AI. She holds a PhD in theoretical machine learning and enjoys turning cutting-edge research into real-world solutions.
Mike Laddin is Senior Vice President & General Manager of EBSCOlearning, a division of EBSCO Information Services. EBSCOlearning offers highly acclaimed online products and services for companies, educational institutions, and workforce development organizations. Mike oversees a team of professionals focused on unlocking the potential of people and organizations with on-demand upskilling and microlearning solutions. He has over 25 years of experience as both an entrepreneur and software executive in the information services industry. Mike received an MBA from the Lally School of Management at Rensselaer Polytechnic Institute, and outside of work he is an avid boater.
Alyssa Gigliotti is a Content Strategy and Product Operations Manager at EBSCOlearning, where she collaborates with her team to design top-tier microlearning solutions focused on enhancing business and power skills. With a background in English, professional writing, and technical communications from UMass Amherst, Alyssa combines her expertise in language with a strategic approach to educational content. Alyssa’s in-depth knowledge of the product and voice of the customer allows her to actively engage in product development planning to ensure her team continuously meets the needs of users. Outside the professional sphere, she is both a talented artist and a passionate reader, continuously seeking inspiration from creative and literary pursuits.
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