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Source: Generative AI and the Future of Education

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Source: Generative AI and the Future of Education

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Explores the need for guardrails and research for AI that is deployed in educational settings, and raises questions of big-picture ethics.

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Curriculum: Pre-made AI Literacy Lessons from Common Sense Media

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Curriculum: Pre-made AI Literacy Lessons from Common Sense Media

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Includes slides, handouts, and teacher guides, all for free.

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Quick, grab-and-go lessons to help your students think critically about AI and its impact.

Artificial intelligence (AI) is rapidly transforming the world we live in. From using generative AI for schoolwork to dealing with misinformation and befriending AI chatbots, students have a lot to consider when it comes to this ever-evolving technology.

This collection of quick lessons (20 minutes or less!) provide an introduction to AI and help address its social and ethical impacts. Through these lessons, students will:

Understand what AI is and how it works

Consider some of its potential benefits and risks

Think critically about how we can be responsible and ethical users of AI

ChatGPT and Beyond: How to Handle AI in Schools

Here's what educators can do as artificial intelligence evolves.

Free Resources to Explore and Use ChatGPT and AI

Get an educator-focused approach to information, concerns, and uses for artificial intelligence.

Transforming Education with AI: What Educators Need to Know

Lessons and Tools for Teaching About Artificial Intelligence

Help your students understand the impacts and uses of AI.

ChatGPT: To Ban or Not to Ban?

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AI Literacy Lessons for Grades 6-12 | Common Sense Education

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Quick, grab-and-go lessons to teach your students how to be ethical and empowered users of artificial intelligence.

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Why AI literacy should be a core competency in education.

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Why AI literacy is now a core competency in education

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A new AI Literacy Framework (AILit) aims to empower learners to navigate an AI-integrated world with confidence and purpose. Here’s what you need to know.

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Source: Refuting the "ChatGPT Causes Brain Damage" Idea

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Source: Refuting the "ChatGPT Causes Brain Damage" Idea

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An analysis of the MIT study (which was featured in a TIME article) that postulated ChatGPT might be eroding students' ability to think critically.

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There's a new ( unreviewed draft of a) scientific article out, examining the relationship between Large Language Model (LLM) use and brain functionality, which many reporters are incorrectly claiming shows proof that ChatGPT is damaging people's brains.

As an educator and writer, I am concerned by the growing popularity of so-called AI writing programs like ChatGPT, Claude, and Google Gemini, which when used injudiciously can take all of the struggle and reward out of writing, and lead to carefully written work becoming undervalued. But as a psychologist and lifelong skeptic, I am forever dismayed by sloppy, sensationalistic reporting on neuroscience, and how eager the public is to believe any claim that sounds scary or comes paired with a grainy image of a brain scan.

So I wanted to take a moment today to unpack exactly what the study authors did, what they actually found, and what the results of their work might mean for anyone concerned about the rise of AI - or the ongoing problem of irresponsible science reporting.

If you don't have time for 4,000 lovingly crafted words, here's the tl;dr.

EEG scans only monitored brain function while writing the essays, not subjects' overall cognitive abilities, or effort at tasks they actually cared about.

Google users were also found to utilize fewer cognitive resources and engage in less memory retrieval while writing their essays in this study, but nobody seems to hand-wring about search engines being used to augment writing anymore.

Changes in cognitive ability & motivation over time were not measured.

The researchers themselves used an AI model to analyze their data, so staunch anti-AI users don't have support for there views here.

If you have the brain space for the full write-up, follow along:

Head author Nataliya Kosmyna and her colleagues at the MIT Media Lab set out to study how the use of large language models (LLMs) like ChatGPT affects students' critical engagement with writing tasks, using electroencephalogram scans to monitor their brains' electrical activity as they were writing. They also evaluated the quality of participants' papers on several dimensions, and questioned them after the fact about what they remembered of their essays.

Each of the study's 54 research subjects were brought in for four separate writing sessions over a period of four months. It was only during these writing tasks that students' brain activity was monitored.

Prior research has shown that when individuals rely upon an LLM to complete a cognitively demanding task, they devote fewer of their own cognitive resources to that task, and use less critical thinking in their approach to that task. Researchers call this process of handing over the burden of intellectually demanding activities to a large language model cognitive offloading, and there is a concern voiced frequently in the literature that repeated cognitive offloading could diminish a person's actual cognitive abilities over time or create AI dependence.

Now, there is a big difference between deciding not to work very hard on an activity because technology has streamlined it, and actually losing the ability to engage in deeper thought, particularly since the tasks that people tend to offload to LLMs are repetitive, tedious, or unfulfilling ones that they're required to complete for work and school and don't otherwise value for themselves. It would be foolhardy to assume that simply because a person uses ChatGPT to summarize an assigned reading for a class that they have lost the ability to read, just as it would be wrong to assume that a person can't add or subtract because they have used a calculator.

However, it's unquestionable that LLM use has exploded across college campuses in recent years and rendered a great many introductory writing assignments irrelevant, and that educators are feeling the dread that their profession is no longer seen as important. I have written about this dread before - though I trace it back to government disinvestment in higher education and commodification of university degrees that dates back to Reagan, not to ChatGPT.

College educators have been treated like underpaid quiz-graders and degrees have been sold with very low barriers to completion for decades now, I have argued, and the rise of students submitting ChatGPT-written essays to be graded using ChatGPT-generated rubrics is really just a logical consequence of the profit motive that has already ravaged higher education. But I can't say any of these longstanding economic developments have been positive for the quality of the education that we professors give out (or that it's helped students remain motivated in their own learning process), so I do think it is fair that so many academics are concerned that widespread LLM use could lead to some kind of mental atrophy over time.
This study, however, is not evidence that any lasting cognitive atrophy has happened. It would take a far more robust, long-term study design tracking subjects' cognitive engagement against a variety of tasks that they actually care about in order to test that.
Rather, Kosmyna and colleagues brought their 54 study participants into the lab four separate times, and assigned them SAT-style essays to write, in exchange for a $100 stipend. The study participants did not earn any grade, and having a high-quality essay did not earn them any additional compensation. There was, therefore, very little personal incentive to try very hard at the essay-writing task, beyond whatever the participant already found gratifying about it.

At the outset of the study, participants were randomly assigned to one of three conditions, which they remained in for the first three writing periods of the study (at the fourth and final writing period, the "brain" and "LLM" groups were switched):

I think it's important to highlight that last detail: subjects in the LLM group could not search for information to support their essays on their own; they had to rely upon OpenAI if they wanted to look up any information.

Whether it was possible for members of the LLM group to refuse to use OpenAI and write the essay entirely on their own, as the members of "brain only" group did, is unclear in the study protocol. As a researcher myself, I will point out that the silent pressure of demand characteristics within a study are nearly always there, so members of the LLM group probably guessed that they were expected to use AI to write their papers regardless. But when we think about how well Kosmyna et. al's findings extend to real life, we do have to keep the artificiality of this in mind.

I will also point out that because this is an experimental study, Kosmyna and her colleagues are attempting to isolate the effect of using an LLM from any other variables that might influence essay quality or brain functioning. This means the study cannot compare individuals who already choose to use LLMs regularly to those who do not, nor can it observe how real LLM users engage with the tools, or measure what effects actual long-term LLM use have.

It would require an extensive, highly expensive longitudinal design with data collection at far more time points to even begin to study larger questions like that. And that's simply not what the research team is setting out to do here. And that's fine! It's important to have well-controlled laboratory studies that look into highly specific, short-term effects of using an LLM! The problem is that many reporters don't seem to be aware of this, and are misreporting the study as being one on the long-term effects of using AI. It's not.

Participants were given 20 minutes to write each of their essays, during which time they were hooked up to an electroencephalogram (or EEG) headset, which tracked electrical activity throughout the brain, in areas associated with word searching, word retrieval, creative demand, use of memory, executive functioning, cognitive engagement, and even motor engagement (because hey, typing up an essay requires more use of your hands that having OpenAI write it does!).

What's been far less reported on is that the essays themselves were heavily analyzed for content, structure, and other markers of writing quality, and participants were asked to discuss their writing process and quizzed on how much they remembered about their essays. These measures make up a huge bulk of the study's outcomes and conclusions, which gets a bit lost when the reporting is all about how AI use impacts subjects brain functioning.

A lot of the meatier conclusions in this study are rooted in regular-old human analysis of written content, and verbal feedback from participants; it's a bit ironic that all the fear-mongering reporting about the dangers of AI falls back on shiny, impressive-looking brain imaging technology to back up its argument when the study itself treats qualitative human feedback as just as important. The public has always found studies more persuasive if they include brain imaging, even when the brain imaging data is highly tentative and flawed. It apparently looks more "sciencey" to people. It's just a bit...odd to overly rely upon brain imaging data to report on a study that's all about the pitfalls of falling for over-hyped tech.

So what did the researchers find? Here's a summary of what the performance from the LLM group looked like, over the course of the four trials:

As you can see, the quality of essays written by the LLM group declined over time, as did the apparent amount of effort they placed into the process of writing. The first LLM-assisted essays had very little in the way of a unique voice, but participants did put some attention into using the OpenAI tool to translate information and augment their writing process. Overall though, they did not feel a strong sense of ownership of their essays and could not quote from their own papers when asked.

The amount of effort that participants placed into their essays declined over time. By the third time the LLM participants came into the laboratory, they essentially relied on the tool to write their essay entirely, and copy-pasted whatever it spit out into their submission. They did not appear to edit their work thoroughly, and what they submitted resembled what ChatGPT would put together by default on its own.

There was a great deal of homogeneity between the essays that LLM participants wrote. In the fourth trial, when they were now asked to write essays using only their brains, members of the LLM group suddenly performed far better, integrating content into their essays more and scoring above average, though they still underperformed compared to the subjects who had been writing the essays with only their brains in the other trials.

Participants in the LLM group were contrasted with both the Google group and the "brain only" group, whose essays on average were better quality. Generally speaking, the participants who were assigned to use only their brains to write their essays wrote more unique essays that could be distinguished from one another readily and actually varied in quality from writer to writer; they could also remember what they had written about and quote from it in detail, and when they switched to using OpenAI in the fourth and final trial of the study, they continued to use a bit more effort. They felt a stronger sense of ownership in their work than the LLM-using group did. (Members of the Google-using group generally fell in the middle, showing more engagement and investment in their essay writing than the OpenAI users, but less than the brain-only group).

I should point out here that the researchers analyzed participants' essays and their interview feedback using AI, which might really shock the most vociferous of anti-AI alarmists who gravitated to this study as proof of ChatGPT's ultimate evil. One of the primary analysis techniques in the study was NLP, or Natural Language Processing, and the team created a specially-trained AI model in order to perform it on their data:

The brain-only group appeared to process information in a slower, more bottom-up fashion ( similar to how Autistics process!) when writing their essays, compared to LLM-users, who worked on their essays in a more rapid, top-down way, filtering through information rather than carefully generating it.

Though the researchers' overall wariness about LLMs being used within the classroom is evident in their article, their analytic techniques reveal that similar technology can be useful when applied to a specific task correctly, and cross-checked against human eyes. Numerous social science researchers now utilize AI to compile their datasets, clean out missing values and errors, perform basic analyses, organize findings into tables, place citations into a proper reference format, and even to generate their initial literature review - including some of the very researchers most concerned about AI! As we read through this work, we ought to bear this in mind.

Let's take a quick look at what the EEG data from the study shows:

Don't these brain imaging results look rigorous and scientific? And they're so difficult for a non-expert to read any results from, particularly when you take into account that most brain imaging studies rely upon heavy filtering and mathematical correction in order to isolate any significant effects because the data collection is so filled with noise and artifacts. Here's what the same brain region looked like, when results are isolated only to the statistically significant effects (the more red the connectivity line shown is, the more significant the association found):

I won't be overly glib here, because the overall work being done in this study is sound - the major bone that I have to pick is with science writers who seem to have no idea how easily neuroimaging results can be fabricated, and seem to not care to know. Overall, Kosmyna and her team did find significant differences in the cognitive engagement of participants while they were writing their essays, in several key areas of the brain:

All of these findings line up with the researchers' overall expectations, and make conceptual sense. When people have the help of OpenAI to write essays (in a completely contrived laboratory setting where the quality of the essay literally does not matter to them at all), they put less attention into the task, try less, don't bother searching their own memories for relevant words or facts, don't exert a ton of self-control into making sure that they are writing at a high caliber, and eventually, after repeated writing exercises, they start just copying and pasting from the AI chat window into the submission box.

My question, though, is so what?

So what if a person who has no personal or external incentive to write an goddamned SAT essay well kinda dials it in?

So what if a person who is told by the researchers to use AI to write a paper winds up using AI, and cares less about the final product as a result?

So what if a person who uses AI to write a paper (again, because they were assigned to do so) reads it less carefully, remembers less of it, and feels like the essay is less "theirs", compared to a person who...actually wrote the essay?

So what if a person who has been brought into a lab to make OpenAI write an essay three times really stops giving a shit by the third time, and puts even less attention into the (boring, unrewarding) task than they did before?

Is this not all a completely rational response to the situation these subjects have been put in? What person would continue to remain cognitively engaged and motivated by a task that has been designed to be both pointless and unenriching for them to take part in? Why would we ever expect a participant to write a beautiful, personalized essay when everything about the assignment pointed them toward doing as efficient and sloppy a job as possible?

These findings do not provide us with any evidence of long-term cognitive decline. Rather, what the study shows is that people do not try very hard when we give them no reason to try very hard. When there is no social recognition for a task, financial reward, professional reward, or enjoyment to be found in it, people do not care about it. That's not brain damage or idiocracy, or even the devaluation of writing. It's a rational choice. When a person does not try at a task that they do not care about, it isn't laziness. It's a logical use of resources. We might disagree with another person's priorities, but what they've done makes perfect sense from where they are sitting.

And we can understand the growing use of Large Language Models in the classroom from this same perspective. Today's generation of college students were told they had to go tens of thousands of dollars into debt to earn a college degree, not because it would provide personal enrichment or particularly help their career, but because holding that credential is pretty much mandatory to find employment in a lot of fields. In 70% of entry-level positions, a bachelor's degree is now required. Students enter the academy out of desperation and economic coercion, and they are not trained to value the struggle of deep learning. And they, quite logically, approach the academic enterprise as a means to an end.

When they arrive on campus, these same jaded students are met with a teaching force that is predominately inexperienced graduate students and underpaid part-timers who are themselves being sorely exploited by a university system that loses more funding and cuts more teaching positions with each passing year.
You can read the full paper (which I highly encourage!) for free here.
These instructors who have been given very few resources to support their teaching and will receive no benefit for a stellar performance show the signs of their incentive structure in everything they do. And so they re-use reading lists they haven't had the time to update since 1980. They "ungrade" and give everybody A's because the students are just there for the piece of paper, and the administrators only care about keeping the enrollment numbers high, so it really doesn't matter anyway. And yes, sometimes they write their exams and grade their papers using ChatGPT. Why shouldn't they? Their luckier colleagues with tenure-track positions are using AI to analyze their data and write their emails, too; in a competitive publish-or-perish environment, each new piece of technology only increases the pressure to streamline, extends the necessity of doing ever more with even less.

So what if a student decides that their class papers do not matter in this environment? What reason does someone have to pour their heart and soul and brain capacity into choosing unique turns of phrase and researching novel examples? Who is going to read their paper? Who is going to care? And what value has been given to writing in such a disposable world?

Participants in the brain-only group had to use more of their working memory and cognitive resources to pen their essays. They had literally no other choice, and no other resources provided to them. And they produced more unique, memorable essays, and felt more invested in their work as a result. Circumstances motivated them to look inward and find out what memories and creative capacities were inside them. And if we want the next generation of writers to emerge with a real investment in the craft of writing and an ability to find the joy in it, we will have to give them reason to look, slowly, and carefully inward.

It is foolish, and defeatist, to say that today's students have brain damage from using ChatGPT. What's damaging are our incentive structures, which point at every moment toward carelessness and speed. But we can set things right at any time, and repair all of the damage that capitalism has done to us. We need only to create real challenges for ourselves that engage our wonderfully whole brains, and reason to believe that our efforts matter.

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There is no evidence that AI causes brain damage. (Not yet anyway.)

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Unpacking the viral, un-peer-reviewed study on LLM use and brain function.

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Source: Stanford Article on Dangers Posed by Chatbots to Young People

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Source: Stanford Article on Dangers Posed by Chatbots to Young People

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Why AI companions and young people can make for a dangerous mix

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Artificial intelligence chatbots designed to act like friends should not be used by children and teens, Stanford Medicine psychiatrist Nina Vasan says.

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Explores how AI can help or hurt your thinking depending on how you use it.

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A balanced look at how AI is likely to impact the field of education, written by a science educator.

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Source: 2025 Microsoft report: AI in Education

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Source: 2025 Microsoft report: AI in Education

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Reports on use of AI by high school/higher ed students and educators, and offers conclusions for best use of AI in education. (Note: this report is by Microsoft and encourages use of their AI, CoPilot.)

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Source: LinkedIn Report on AI and the Economy

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Source: LinkedIn Report on AI and the Economy

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A glimpse at what workforce-readiness needs to look like in the AI age.

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An animated video suitable for both teachers and students, showing how AI learns and why no one (even their coders) really knows how the "big algorithms" work.

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Bonus: How do middle schoolers feel about AI?

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Bonus: How do middle schoolers feel about AI?

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Middle schoolers are well aware of AI’s pitfalls, including its tendency to get facts wrong and its potential to stifle their own learning.

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Parents’ Ultimate Guide to Generative AI

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Parents’ Ultimate Guide to Generative AI - Learn more and get Common Sense Media’s research-backed tips.

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Mind to Machine Part 1: A Brief History of AI

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Source: AI Impact on Pediatric Mental Health

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Source: AI Impact on Pediatric Mental Health

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Artificial intelligence impacts in education and pediatric mental health - PubMed

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Although artificial intelligence has the potential to revolutionize education at all levels, its use as an enhancement, not replacement, to current educational strategies is imperative. Both parents and students need to understand the limitations of artificial intelligence in education, and simultan …

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Source: MagicSchool.AI on Privacy and Security

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Source: MagicSchool.AI on Privacy and Security

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MagicSchool.AI gets high rankings for data security and privacy.

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AI in education is an emerging and uncharted technology, with the issue of privacy being a top concern. At MagicSchool, we prioritize the privacy of our users. To learn more, please refer to our comprehensive privacy policy.

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How AI Moderation Keep Students Safe | MagicSchool

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Explore how AI moderation helps protect student safety, ensure responsible AI use, and give districts control over their MagicSchool tools. Read the blog post now!

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Explains and gives real-world examples of how AI bias is created and the problems it can cause.

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AI bias refers to biased results due to human biases that skew original training data or AI algorithms—leading to distorted and potentially harmful outputs.

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Bonus: How does AI affect the environment?

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Bonus: How does AI affect the environment?

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MIT News | Massachusetts Institute of Technology

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Explained: Generative AI’s environmental impact

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MIT News explores the environmental and sustainability implications of generative AI technologies and applications.

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Bonus: The Risks of AI Power Concentration

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Bonus: The Risks of AI Power Concentration

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"AI offers unprecedented opportunities to improve human life. However, these benefits come with significant risks, particularly when the power and control of AI are concentrated in the hands of a wealthy elite and a few powerful organizations. This concentration of AI power poses several dangers, including economic inequality, erosion of privacy, biased decision-making, and the stifling of innovation."

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The Risks of AI Power Concentration: A Call for Democratization

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The rapid advancement of artificial intelligence (AI) has the potential to transform society in profound ways. From healthcare and education to finance and transportation, AI offers unprecedented opportunities to improve human life.

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How AI Models Steal Creative Work — and What to Do About It | Ed Newton-Rex | TED

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How Will AI Affect the Global Workforce?

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AI-related innovation may cause near-term job displacement while also ultimately creating new opportunities elsewhere.

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Exploring privacy issues in the age of AI | IBM

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AI arguably poses a greater data privacy risk than earlier technological advancements, but the right software solutions can address AI privacy concerns.

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Bonus: MagicSchool.AI Student Contract template

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Bonus: MagicSchool.AI Student Contract template

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Copy of Kellogg MagicSchool Student Contract 

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Bonus: MagicSchool.AI Parent Letter template

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Bonus: MagicSchool.AI Parent Letter template

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Copy of Kellogg MagicSchool Parent Letter (Pilot)

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Source: AI-Driven Innovations for Disabled People

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Source: AI-Driven Innovations for Disabled People

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Tyler Weitzman, cofounder, President and Head of AI at .
The WHO estimates that more than 2.5 billion disabled people will need one or more assistive technologies in 2030. Yet almost a billion of them can't access these products. Considering how globalization is increasing, this means we are denying an entire community from enjoying the same services as everyone else.

As AI systems have grown in popularity and complexity, however, we are seeing more AI-powered solutions that can help people with mental, physical, visual or hearing impairments perform tasks, both everyday and complex. I believe that by making AI technologies more widely accessible, many businesses can change the lives of individuals with disabilities. Here are four ways businesses using AI technology can empower (or are already empowering) individuals with disabilities.

1. Easing Communication

AI voice technologies, such as Siri, Alexa and Echo, help make communication easier for people with impairments. These programs can describe text and images to people with visual disabilities, and text-to-speech (or vice versa) technologies can help those with brain injuries communicate or understand things more easily. One such app is Google's Parrotron, an AI tool that enables speech-impaired individuals to translate their distorted speech patterns into fluent conversations.

Businesses can utilize these AI tools to create a more inclusive and collaborative work culture that includes employees regardless of their disabilities. By providing these individuals with effective speech transcription tools, business leaders can help them participate equally in collaborative settings and share ideas across departments and teams.

Businesses can also use AI-powered voice technology to improve their customer support. For instance, AI chatbots can help your customer service teams communicate with customers with disabilities, understand their needs more readily, and fulfill them more effectively. This helps create better experiences, which can ultimately lead to higher customer loyalty.

2. Learning Opportunities

Everyone should be able to avail of any service regardless of their disabilities. AI technology can help ensure this inclusivity. For example, just like subtitles let people with hearing impairments understand a movie, braille helps visually impaired individuals read. You can now find AI-powered braille tutor apps on the internet that assist disabled individuals in learning braille and accessing education at different levels. These apps use optical character recognition (OCR) to transform physical braille characters into digital text.

Business leaders can leverage AI-powered solutions to create a continuous learning and development culture. For example, you can implement personalized learning paths to help employees find relevant training courses and materials according to their skills. Moreover, you can use AI tools to provide continuous feedback to employees based on their performance to help them learn from their mistakes.

Leaders can also use AI tools to identify skill gaps within the company, learn about an employee's competencies, and develop targeted training programs to promote upskilling opportunities. There are even options available for using virtual reality (VR) and augmented reality (AR) to offer interactive learning experiences to employees.

I believe it is imperative for businesses to provide employees with disabilities with personalized learning experiences that help them acquire the skills and knowledge needed to succeed in their daily roles. This creates a diverse workforce that empowers everyone. With the proper implementation, businesses can foster a growth-focused mindset in their workforce without discrimination, eventually boosting the bottom line.

3. Promoting An Independent Lifestyle

AI is opening doors for disabled individuals to live independent personal and professional lifestyles. For example, many AI programs help businesses create effective and efficient workflows with virtual assistants like Google Assistant or Amazon Alexa, which can schedule meetings, set reminders and automate many voice tasks. Business leaders can also integrate AI-powered chatbots into different work-related apps to assist employees in different tasks. Microsoft Bing's integration with Skype is one such example: You can simply ask a question from Bing on Skype, and it will provide an answer immediately.

Other AI tools that can ease workflow include robotic process automation (RPA), which can automate repetitive tasks; AI-integrated recommendation systems, which can be used to retrieve personalized suggestions from data; and AI computer vision applications, which can identify and interpret visual data through object detection, image recognition, facial recognition and quality control. Businesses can also benefit from AI-integrated cybersecurity tools to detect threats proactively and eliminate them in real time.

By utilizing these tools, every employee-including those with disabilities-can streamline manual tasks and free up time to focus on more critical work. This can also help leaders attract and retain diverse talent unhindered by the barriers they might otherwise face.

4. Connection To The Outside World

In this fast-paced, globalized world, it's as important as ever for people to feel connected with each other. Fortunately, AI technology has introduced new ways to help people with disabilities feel linked to their surroundings. For example, Microsoft's Cortana can assist visually impaired individuals in navigating their devices through voice control. Programs like RogerVoice and Ava utilize AI to transcribe group conversations for those with hearing impairments, including punctuation, the sender's name and important words from the user's dictionary.

Businesses can employ these AI-powered virtual assistants to help disabled individuals attend and schedule meetings, answer emails and collaborate with others like everyone else. Translation tools are also available to assist employees who speak different languages in communicating with each other.

Inclusion Regardless Of Ability

People with disabilities have the same right to enjoy life and succeed at their professional goals as everyone else. They should be empowered to work independently, communicate across teams and learn new skills. Fortunately, AI has been helping business leaders enhance accessibility and inclusivity in their workplaces. Consider how you can incorporate some of these tools in your workplace, not only to give your current employees more paths to success but also to attract and retain a talented and diverse employee pool.

Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?

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Council Post: Empowering Individuals With Disabilities Through AI Technology

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AI has been helping business leaders enhance accessibility and inclusivity in their workplaces.

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Bonus: A warning on companion chatbot use

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Bonus: A warning on companion chatbot use

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AI Companies’ Race for Engagement Has a Body Count | TechPolicy.Press

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News reports of deaths related to AI chatbots should be setting off alarm bells for all of us, writes Common Sense Media founder and CEO Jim Steyer.

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AI ethics Professional Development slides

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AI ethics Professional Development slides

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Ethics in AI Professional Development Slides