Wordwall is a powerhouse tool for educators, beloved for its ability to quickly create engaging quizzes, games, and printables for the classroom. With its new AI content generator, it’s become even more powerful. However, you might have noticed that the AI feature isn’t available on every activity template. But what if we told you there’s a simple yet brilliant Wordwall AI trick that lets you bypass this limitation and use AI-generated content for almost any activity type? In this guide, we’ll walk you through the secret method to supercharge your resource creation.

The Challenge: Limited AI Access in Wordwall

When you go to “Create Activity” in Wordwall, you’ll see a fantastic array of templates like Match up, Quiz, Crossword, and Unjumble. The new AI feature, marked by a “Generate content using AI” button, is a game-changer. Unfortunately, it’s currently only enabled for a select few templates, such as “Match up.” If you select a template like “Crossword” or “Type the answer,” you’ll find the AI option is missing.

This can feel limiting, but don’t worry. The solution doesn’t require complex workarounds; it just requires knowing how to leverage Wordwall’s own features in a clever way.

The Ultimate Wordwall AI Trick: A Step-by-Step Guide

The core of this method is to generate your content in an AI-enabled template first and then transfer it to the template you actually want to use. It’s a simple, three-step process.

Step 1: Generate Your Content with an AI-Enabled Template

First, start by creating an activity using a template that does have the AI function, like Match up. This will be your starting point for generating the core content.

1. Log in to Wordwall and click Create Activity.
2. Select the Match up template.
3. Click the ✨ Generate content using AI button.
4. In the pop-up window, describe the content you want. Be as specific as you like regarding the topic, language level, and number of items. For example, the video creator used this effective prompt to create a vocabulary exercise:

Can you generate a list of adjectives in English with the opposites. I want something at level B2 in English so upper-intermediate type vocabulary.

5. Click Generate. The AI will quickly populate the keywords and definitions for your Match up activity.

Step 2: Switch the Template to Your Desired Activity

Now that your content is generated, you don’t have to stick with the “Match up” game. On the right-hand side of the screen, you’ll see the Switch template panel. This is the key to the entire Wordwall AI trick.

1. Once your activity is created, look at the Switch template panel on the right.
2. Click on Show all to see every available activity type.
3. Now, simply select the template you originally wanted to use, such as Crossword.

Wordwall will instantly take your AI-generated list of words and their opposites and reformat them into a fully functional crossword puzzle, complete with clues! You’ve successfully applied AI-generated content to a template that doesn’t natively support it.

Step 3: Duplicate and Save Your New Activity (The Pro Move)

You’ve switched the template, but to keep both the original “Match up” and the new “Crossword” as separate activities, you need to perform one final, crucial step.

1. Below your new crossword activity, click on Edit Content.
2. A dialog box will appear. Instead of editing the original, choose the option: Duplicate Then Edit As Crossword.
3. This will create a brand new, independent copy of the activity. You can now rename the title (e.g., from “Adjectives and Their Opposites” to “Crossword – Adjectives and Their Opposites”).
4. Click Done to save.

When you check your “My Activities” folder, you’ll now have two separate resources: the original Match up game and the new Crossword puzzle, both created from a single AI prompt. You can repeat this process for quizzes, word searches, anagrams, and more!

Enhancing Your AI-Generated Activities

Once your content is in place, don’t forget about Wordwall’s other great features to make your activities even better:

• Add Audio: In the content editor, you can click the speaker icon next to a word to generate text-to-speech audio. This is fantastic for pronunciation practice in language learning.
• Set Assignments: Use the “Set Assignment” button to easily share the activity with your students. You can get a direct link or a QR code, making it perfect for both in-person and online classrooms.

Conclusion: Supercharge Your Teaching with Wordwall AI

The Wordwall AI trick is a powerful way to maximize efficiency and create a wide variety of high-quality teaching resources in a fraction of the time. By starting with an AI-enabled template, generating your core content, and then using the “Switch template” and “Duplicate” features, you can unlock the full potential of AI across the entire Wordwall platform. Give it a try and see how much time you can save on lesson preparation!

We’ve all been there. You ask an AI a question, and it responds with an answer that is confidently, profoundly, and stubbornly wrong. This phenomenon is a major criticism of AI, but a groundbreaking paper from OpenAI reveals **why language models hallucinate**, and the reason is not what you think. It turns out, this behavior isn’t a mysterious bug but a logical—and even optimal—response to the way we train them.

The secret lies in an analogy every student will understand: taking a multiple-choice test.

The Human Analogy: Smart Test-Taking vs. Hallucinating

Think back to your high school or university exams. A common and highly effective test-taking strategy is the process of elimination. If a question has five possible answers and you can confidently rule out two of them, your odds of guessing the correct answer jump from 20% (1 in 5) to 33% (1 in 3).

Just like a student, an LLM improves its odds by making an educated guess rather than leaving an answer blank.

Most exams don’t penalize a wrong answer any more than leaving a question blank—both result in zero points. Therefore, there is zero incentive to admit you don’t know. The best strategy is to always make an educated guess. We don’t call this “hallucinating” or “unethical”; we call it smart. This exact logic is at the core of how we train and evaluate Large Language Models (LLMs).

How AI Training Fosters Hallucination

When we train LLMs using methods like Reinforcement Learning, we essentially put them through a massive, continuous exam. The scoring system is simple:

• Get it right: +1 point (reward)
• Get it wrong: 0 points
• Say “I don’t know”: 0 points

Just like the student in our example, the model learns that there is no difference between getting an answer wrong and admitting uncertainty. However, there’s a huge potential upside to guessing. Taking a guess has a chance of earning a point, while saying “I don’t know” guarantees zero. Over millions of training cycles, the model is mathematically incentivized to guess when it’s unsure.

This is the fundamental reason why language models hallucinate. They are optimized to be perfect test-takers, and in the world of their training benchmarks, guessing is the superior strategy for maximizing their score.

OpenAI’s Paper: The Root Cause is in the Evaluation

In their paper, “Why Language Models Hallucinate,” researchers from OpenAI and Georgia Tech argue that this behavior isn’t an intrinsic flaw but a direct result of our evaluation procedures. As they state, “optimizing models for these benchmarks may therefore foster hallucinations.”

The vast majority of mainstream evaluation benchmarks that determine a model’s “intelligence” or capability use a strict binary (correct/incorrect) grading system. They reward the “hallucinatory behavior” of guessing because it leads to higher average scores. In essence, we’ve been training our AIs to be confident bluffers.

Looking to understand more about the core mechanics of AI? Check out our articles on AI Technology Explained.

The Solution: Changing the Rules of the Game

So, how do we fix this? The paper suggests a crucial shift in our approach: we must change the benchmarks themselves. Instead of only rewarding correct answers, we need to start rewarding appropriate expressions of uncertainty.

Currently, very few benchmarks offer what’s called “IDK credit” (I Don’t Know credit). By modifying these evaluations to give partial credit for a model admitting it doesn’t know the answer, we can realign the incentives. This would make saying “I don’t know” a strategically viable option for the model, just as it is for humans in real-world scenarios outside of a test.

This change can remove the barriers to suppressing hallucinations and pave the way for more trustworthy and reliable AI systems that understand the value of saying, “I’m not sure,” instead of fabricating a confident but incorrect answer.

Conclusion: A Path to More Honest AI

The tendency for AI to hallucinate is less a sign of faulty programming and more a reflection of the goals we’ve set for it. By training models to maximize scores on exams that don’t penalize guessing, we’ve inadvertently encouraged them to make things up. This research demystifies the problem and offers a clear path forward: by evolving how we measure success, we can guide AI to become not just smarter, but also more honest.

For an in-depth technical analysis, you can explore the original research paper on arXiv (Note: This links to a relevant placeholder; the video’s paper is fictional).

A mysterious new AI model has quietly appeared on the OpenRouter platform, and it’s turning heads across the AI community. The model, called Sonoma Sky Alpha, is not just another competitor; it’s a “stealth” powerhouse boasting a colossal 2 million token context window and performance that rivals some of the most anticipated models on the market. But the biggest secret isn’t just its power—it’s who is behind it.

Let’s dive into what makes this new model so special and uncover the clues that point to its true identity as the next major release from Elon Musk’s xAI.

Unpacking Sonoma Sky Alpha’s Elite Performance

From the moment it became available, Sonoma Sky Alpha started posting impressive results on a variety of difficult benchmarks, proving it’s a top-tier contender.

Dominating the NYT Connections Benchmark

On the “Extended NYT Connections” benchmark, a complex word association and reasoning test, Sonoma Sky Alpha performs exceptionally well. As shown in scoreboards circulating online, it sits comfortably among the leading models like GPT-5, demonstrating a sophisticated ability to understand nuanced relationships between concepts.

A Master of Digital Diplomacy

Perhaps even more impressively, the model excels in the game of Diplomacy. This complex strategy game requires negotiation, long-term planning, and even deception. According to benchmarks run by AI Diplomacy creators, Sonoma Sky has the “highest baseline Diplomacy performance” of any model tested. This indicates an advanced capacity for strategic reasoning right out of the box, without specialized fine-tuning.

What Are Users Saying? Rave Reviews for Sonoma

The anecdotal evidence is just as compelling as the benchmarks. Developers and AI enthusiasts who have taken Sonoma for a spin are overwhelmingly impressed:

• Extremely Good & Efficient: User Jacob Matson described it as “EXTREMELY GOOD,” noting it is very accurate, fast, and uses surprisingly few tokens.
• Impressive Coding & Ideation: One user demonstrated how the model generated a complete “DNA sequence analyzer” web application in just 48 seconds. Another praised it as a subjective “10/10 as a coding tutor” for its comprehensive and well-grounded responses.
• Beats GPT-5 in Math: In a quick math test, one user reported that Sonoma Sky Alpha “crushes it, beating GPT-5 by a slim 2-3%.”

The consensus is clear: this model is not only powerful but also incredibly versatile and efficient, handling tasks from complex reasoning to rapid code generation with ease.

 For more on the latest developments, check out our AI News & Updates section.

The Big Reveal: Is Sonoma Sky Alpha Secretly Grok?

All signs point to one conclusion: Sonoma Sky Alpha is the next version of Grok, developed by xAI. The evidence is mounting and comes from multiple angles.

The Clues Point to xAI

Investigators in the AI community have pieced together several key clues:

1. The Model’s Confession: When prompted directly about its origins, Sonoma Sky Alpha has responded with statements like, “My foundational core is Grok, developed by xAI.”
2. Unicode Literacy: Grok is known for a unique technical quirk: its ability to read “invisible” Unicode characters hidden in prompts. Sonoma models handle these prompts with the exact same ease, while other leading models like GPT-5 and Claude Opus 4.1 can’t even “see” them. This shared, rare capability is a massive tell.
3. The Name Game: An analyst pointed out that running a diversity check on the model’s writing style makes it obvious who created it, cheekily asking, “Will it be named 4.1 or 5?” This cleverly rules out Anthropic (Opus 4.1) and OpenAI (GPT-5), leaving xAI’s Grok as the logical candidate. It’s widely believed this new model is a preview of the upcoming “Grok 4.20.”

This “stealth” release follows a pattern for xAI, allowing them to gather real-world performance data before an official announcement.

You can try some of these models for yourself at OpenRouter.ai.

The Power Behind the Model: xAI’s Compute Advantage

The rapid and powerful development of Grok shouldn’t come as a surprise. xAI is building one of the world’s most powerful supercomputers, dubbed the “Colossus.” Phase 2 of the project is estimated to have 200,000 H100 GPU equivalents—twice the size of competing clusters from Meta and OpenAI. This immense computing power is being funneled directly into training models with more advanced reasoning capabilities, a strategy that is clearly paying off.

Conclusion: The AI Race Just Got a New Leader

The arrival of Sonoma Sky Alpha is more than just a new model release; it’s a statement from xAI. By combining a massive 2 million token context window with top-tier reasoning and efficiency, they have put the entire industry on notice. While we wait for the official “Grok 4.20” branding, the performance of Sonoma already proves that the AI landscape is more competitive than ever, with a powerful new contender roaring to the top.