Young adults growing up in the attention economy — preparing for adult life, with social media and chatbots competing for their attention — can easily fall into unhealthy relationships with digital platforms. But what if chatbots weren’t mere distractions from real life? Could they be designed humanely, as moral partners whose digital goal is to be a social guide rather than an addictive escape?

At MIT, a friendship between two professors — one an anthropologist, the other a computer scientist — led to creation of an undergraduate class that set out to find the answer to those questions. Combining the two seemingly disparate disciplines, the class encourages students to design artificial intelligence chatbots in humane ways that help users improve themselves.

The class, 6.S061/21A.S02 (Humane User Experience Design, a.k.a. Humane UXD), is an upper-level computer science class cross-listed with anthropology. This unique cross-listing allows computer science majors to fulfill a humanities requirement while also pursuing their career objectives. The two professors use methods from linguistic anthropology to teach students how to integrate the interactional and interpersonal needs of humans into programming.

Professor Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Professor Graham Jones, an anthropologist whose research focuses on communication, created Humane UXD last summer with a grant from the MIT Morningside Academy for Design (MAD). The MIT MAD Design Curriculum Program provides funding for faculty to develop new classes or enhance existing classes using innovative pedagogical approaches that transcend departmental boundaries.

The Design Curriculum Program is currently accepting applications for the 2026-27 academic year; the deadline is Friday, March 20.

Jones and Satyanarayan met several years ago when they co-advised a doctoral student’s research on data visualization for visually impaired people. They’ve since become close friends who can pretty much finish one another’s sentences.

“There’s a way in which you don’t really fully externalize what you know or how you think until you’re teaching,” Jones says. “So, it’s been really fun for me to see Arvind unfurl his expertise as a teacher in a way that lets me see how the pieces fit together — and discover underlying commonalities between our disciplines and our ways of thinking.”

Satyanarayan continues that thought: “One of the things I really enjoyed is the reciprocal version of what Graham said, which is that my field — human-computer interaction — inherited a lot of methods from anthropology, such as interviews and user studies and observation studies. And over the decades, those methods have gotten more and more watered down. As a result, a lot of things have been lost.

“For instance, it was very exciting for me to see how an anthropologist teaches students to interview people. It’s completely different than how I would do it. With my way, we lose the rapport and connection you need to build with your interview participant. Instead, we just extract data from them.”

For Jones’ part, teaching with a computer scientist holds another kind of allure: design. He says that human speech and interaction are organized into underlying genres with stable sets of rules that differentiate an interview at a cocktail party from a conversation at a funeral.

“ChatGPT and other large language models are trained on naturally occurring human communication, so they have all those genres inside them in a latent state, waiting to be activated,” he says.

“As a social scientist, I teach methods for analyzing human conversation, and give students very powerful tools to do that. But it ends up usually being an exercise in pure research, whereas this is a design class, where students are building real-world systems.”

The curriculum appears to be on target for preparing students for jobs after graduation. One student sought permission to miss class for a week because he had a trial internship at a chatbot startup; when he returned, he said his work at the startup was just like what he was learning in class. He got the job.

The sampling of group projects below, built with Google’s Gemini, demonstrates some of what’s possible when, as Jones says, “there’s a really deep intertwining of the technology piece with the humanities piece.” The students’ design work shows that entirely new ways of programming can be conceptualized when the humane is made a priority.

The bots demonstrate clearly that an interdisciplinary class can be designed in such a way that everyone benefits: Students learn more and differently; they can fulfill a non-major course requirement by taking a class that is directly beneficial to their careers; and long-term faculty partnerships can be forged or strengthened.

Team Pond

One project promises to be particularly useful for graduating seniors. Pond is designed to help young college graduates adapt to the challenges of independent adult life. Team Pond configured the chatbot not to simply parrot the user, or to sycophantically praise wrong answers. Instead, Pond provides advice to help with “adulting” (behaving as a responsible adult).

“Pond is built to be your companion from college life into post-college life, to help you in your transition from being a small fish in a small pond to being a small fish in a very big pond,” says sophomore Mary Feliz.

“College is very much a high-proximity and high-context environment, in the sense that everybody around you is going through the same thing, and it’s easy to build relationships or find opportunities, because there are structured pathways that you have access to,” explains graduate student Emaan Khan. “Post-grad life is low-context. You’re not always surrounded by your peers or your professors. It’s no-proximity also, in the sense that you don’t have opportunities at your doorstep. Pond is a tool to help empower you to access certain opportunities, or learn how to navigate.”

Pond’s developers are graduate student Claire Camacho, Harvard University graduate student Charlotte Cheah, Feliz, and Khan. They trained Pond to offer expertise in three areas: social life, professional life, and adult skills.

The students demonstrated Pond’s social communication by asking how to start conversations in a hobby group. Next, they exhibited Pond’s adulting communication by typing in a question.

“My landlord has asked me personal questions that make me uncomfortable. What should I do?”

Appearing in Pond’s textbox was this response: “Politely set boundaries. You are not obligated to answer personal questions. You can gently redirect the conversation back to property-related topics. Know your rights: familiarize yourself with tenant rights in your city or state regarding privacy and landlord conduct.”

When asked how to do that, Pond offered specific instructions: “You can try saying phrases like: ‘I prefer to keep our conversations focused on the property,’ and ‘let’s stick to topics related to the apartment, please.’ Always redirect the conversation back to the apartment or a legitimate maintenance issue. Keep your tone polite but firm. Document any conversations if needed.”

Pond also offered a role-playing scenario to help the user learn what polite-but-firm language might be in that situation.

“The ethos of the practice mode is that you are actively building a skill, so that after using Pond for some time, you feel confident that you can swim on your own,” Khan says. The chatbot uses a point system that allows users to graduate from a topic, and a treasure chest to store prizes, elements added to boost the bot’s appeal.

Team News Nest

Another of the projects, News Nest, provides a sophisticated means of helping young people engage with credible news sources in a way that makes it fun. The name is derived from the program’s 10 appealing and colorful birds, each of which focuses on a particular area of news. If you want the headlines, you ask Polly the Parrot, the main news carrier; if you’re interested in science, Gaia the Goose guides you. The flock also includes Flynn the Falcon, sports reporter; Credo the Crow, for crime and legal news; Edwin the Eagle, a business and economics news guide; Pizzazz the Peacock for pop and entertainment stories; and Pixel the Pigeon, a technology news specialist.

News Nest’s development team is made up of MIT seniors Tiana Jiang and Krystal Montgomery, and junior Natalie Tan. They intentionally built News Nest to prevent “doomscrolling,” provide media transparency (sources and political leanings are always shown), and they created a clever, healthy buffer from emotional manipulation and engagement traps by employing birds rather than human characters.

Team M^3 (Multi-Agent Murder Mystery)

A third team, M^3, decided to experiment with making AI humane by keeping it fun. MIT senior Rodis Aguilar, junior David De La Torre, and second-year Deeraj Pothapragada developed M^3, a social deduction multi-agent murder mystery that incorporates four chatbots as different personalities: Gemini, OpenAI’s ChatGPT, xAI’s Grok, and Anthropic’s Claude. The user is the fifth player. 

Like a regular murder mystery, there are locations, weapons, and lies. The user has to guess who committed the murder. It’s very similar to a board or online game played with real players, only these are enhanced AI opponents you can’t see, who may or may not tell the truth in response to questions. Users can’t get too involved with one chatbot, because they’re playing all four. Also, as in a real life murder mystery game, the user is sometimes guilty.

Photonic chips use light to process data instead of electricity, enabling faster communication speeds and greater bandwidth. Most of that light typically stays on the chip, trapped in optical wires, and is difficult to transmit to the outside world in an efficient manner.

If a lot of light could be rapidly and precisely beamed off the chip, free from the confines of the wiring, it could open the door to higher-resolution displays, smaller Lidar systems, more precise 3D printers, or larger-scale quantum computers.

Now, researchers from MIT and elsewhere have developed a new class of photonic devices that enable the precise broadcasting of light from the chip into free space in a scalable way.

Their chip uses an array of microscopic structures that curl upward, resembling tiny, glowing ski jumps. The researchers can carefully control how light is emitted from thousands of these tiny structures at once.

They used this new platform to project detailed, full-color images that are roughly half the size of a grain of table salt. Used in this way, the technology could aid in the development of lightweight augmented reality glasses or compact displays.

They also demonstrated how photonic “ski jumps” could be used to precisely control quantum bits, or qubits, in a quantum computing system.

“On a chip, light travels in wires, but in our normal, free-space world, light travels wherever it wants. Interfacing between these two worlds has long been a challenge. But now, with this new platform, we can create thousands of individually controllable laser beams that can interact with the world outside the chip in a single shot,” says Henry Wen, a visiting research scientist in the Research Laboratory of Electronics (RLE) at MIT, research scientist at MITRE, and co-lead author of a paper on the new platform.

He is joined on the paper by co-lead authors Matt Saha, of MITRE; Andrew S. Greenspon, a visiting scientist in RLE and MITRE; Matthew Zimmermann, of MITRE; Matt Eichenfeld, a professor at the University of Arizona; senior author Dirk Englund, a professor in the MIT Department of Electrical Engineering and Computer Science and principal investigator in the Quantum Photonics and Artificial Intelligence Group and the RLE; as well as others at MIT, MITRE, Sandia National Laboratories, and the University of Arizona. The research appears today in Nature.

A scalable platform

This work grew out of the Quantum Moonshot Program, a collaboration between MIT, the University of Colorado at Boulder, the MITRE Corporation, and Sandia National Laboratories to develop a novel quantum computing platform using the diamond-based qubits being developed in the Englund lab.

These diamond-based qubits are controlled using laser beams, and the researchers needed a way to interact with millions of qubits at once.

“We can’t control a million laser beams, but we may need to control a million qubits. So, we needed something that can shoot laser beams into free space and scan them over a large area, kind of like firing a T-shirt gun into the crowd at a sports stadium,” Wen says.

Existing methods used to broadcast and steer light off a photonic chip typically work with only a few beams at once and can’t scale up enough to interact with millions of qubits.

To create a scalable platform, the researchers developed a new fabrication technique. Their method produces photonic chips with tiny structures that curve upward off the chip’s surface to shine laser beams into free space.

They built these tiny “ski jumps” for light by creating two-layer structures from two different materials. Each material expands differently when it cools down from the high fabrication temperatures.

The researchers designed the structures with special patterns in each layer so that, when the temperature changes, the difference in strain between the materials causes the entire structure to curve upward as it cools.

This is the same effect as in an old-fashioned thermostat, which utilizes a coil of two metallic materials that curl and uncurl based on the temperature in the room, triggering the HVAC system. “Both of these materials, silicon nitride and aluminum nitride, were separate technologies. Finding a way to put them together was really the fabrication innovation that enables the ski jumps. This wouldn’t have been possible without the pioneering contributions of Matt Eichenfield and Andrew Leenheer at Sandia National Labs,” Wen says.

On the chip, connected waveguides funnel light to the ski jump structures. The researchers use a series of modulators to rapidly and precisely control how that light is turned on and off, enabling them to project light off the chip and move it around in free space.

Painting with light

They can broadcast light in different colors and, by tweaking the frequencies of light, adjust the density of the pattern that is emitted. In this way, they can essentially paint pictures in free space using light.

“This system is so stable we don’t even need to correct for errors. The pattern stays perfectly still on its own. We just calculate what color lasers need to be on at a given time and then turn it on,” he says.

Because the individual points of light, or pixels, are so tiny, the researchers can use this platform to generate extremely high-resolution displays. For instance, with their technique, 30,000 pixels can be fit into the same area that can hold only two pixels used in smartphone displays, Wen says.

“Our platform is the ideal optical engine because our pixels are at the physical limit of how small a pixel can be,” he adds.

Beyond high-resolution displays and larger quantum computers with diamond-based qubits, the method could be used to produce Lidars that are small enough to fit on tiny robots.

It could also be utilized in 3D printing processes that fabricate objects using lasers to cure layers of resin. Because their chip generates controllable beams of light so rapidly, it could greatly increase the speed of these printing processes, allowing users to create more complex objects.

In the future, the researchers want to scale their system up and conduct additional experiments on the yield and uniformity of the light, design a larger system to capture light from an array of photonic chips with “ski jumps,” and conduct robustness tests to see how long the devices last.

“We envision this opening the door to a new class of lab-on-chip capabilities and lithographically defined micro-opto-robotic agents,” Wen says.

This research was funded, in part, by the MITRE Quantum Moonshot Program, the U.S. Department of Energy, and the Center for Integrated Nanotechnologies.

Have you ever seen a really creepy targeted ad online? One that revealed just how much these companies know about your life? It's unsettling enough to see how much companies know about you—but now we have confirmation that the government is also tapping the advertising surveillance machine to get your data. We're explaining the dangers of targeted advertising and location tracking, and the latest in the fight for privacy and free speech online, with our EFFector newsletter.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers a victory for protesters seeking to hold police accountable, a troubling conflict over the Department of Defense's use of AI, and how advertising surveillance enables government surveillance.

Prefer to listen in? Big news: EFFector is now available on all major podcast platforms! In this episode we chat with EFF Staff Attorney Lena Cohen about how targeted advertising can reveal your location to federal law enforcement. You can find the episode and subscribe in your podcast player of choice: 

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Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?

Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon...

Surging oil and gas prices could increase inflation — a bad omen for a renewables industry that recently canceled projects because of higher costs.

A federal judge says the agency needs to comply with a December court order to restart a program that helps states prepare for natural disasters.

The leader of Climate United is taking a position with a wealth management firm.

Getting data centers to generate their own electricity may ease one obstacle to public acceptance of data centers. But it creates new hurdles.

Five senators told the Department of Homeland Security inspector general that the Federal Emergency Management Agency has ignored lawmaker queries.

Gov. Jeff Landry said the state is close to a settlement with the oil major that would help restore the Bayou State’s disappearing coast.

The Florida Legislature passed the measure that at one point was strongly opposed by the state’s insurance commissioner, who then dropped his opposition after the bill was tweaked on the Senate floor.

The higher average temperature was driven primarily by the area west of the Mississippi River, which largely missed out on winter this year, said a NOAA scientist.

As temperatures rise aboveground, the number of subway riders reporting uncomfortable conditions increases, says a new study.

Credits and offsets in Alberta’s carbon market, known as the TIER system, have fallen to $22.53, according to a price tracker.

The strategy aims to help mobilize $767 billion a year in investment in clean energy before 2030.

MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques.

Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.

In the end, the system automatically generates a set of files that can be fed into classical planning software, which computes a plan to achieve the goal. This two-step system generated plans with an average success rate of about 70 percent, outperforming the best baseline methods that could only reach about 30 percent.

Importantly, the system can solve new problems it hasn’t encountered before, making it well-suited for real environments where conditions can change at a moment’s notice.

“Our framework combines the advantages of vision-language models, like their ability to understand images, with the strong planning capabilities of a formal solver,” says Yilun Hao, an aeronautics and astronautics (AeroAstro) graduate student at MIT and lead author of an open-access paper on this technique. “It can take a single image and move it through simulation and then to a reliable, long-horizon plan that could be useful in many real-life applications.”

She is joined on the paper by Yongchao Chen, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS); Chuchu Fan, an associate professor in AeroAstro and a principal investigator in LIDS; and Yang Zhang, a research scientist at the MIT-IBM Watson AI Lab. The paper will be presented at the International Conference on Learning Representations.

Tackling visual tasks

For the past few years, Fan and her colleagues have studied the use of generative AI models to perform complex reasoning and planning, often employing large language models (LLMs) to process text inputs.

Many real-world planning problems, like robotic assembly and autonomous driving, have visual inputs that an LLM can’t handle well on its own. The researchers sought to expand into the visual domain by utilizing vision-language models (VLMs), powerful AI systems that can process images and text.

But VLMs struggle to understand spatial relationships between objects in a scene and often fail to reason correctly over many steps. This makes it difficult to use VLMs for long-range planning.

On the other hand, scientists have developed robust, formal planners that can generate effective long-horizon plans for complex situations. However, these software systems can’t process visual inputs and require expert knowledge to encode a problem into language the solver can understand.

Fan and her team built an automatic planning system that takes the best of both methods. The system, called VLM-guided formal planning (VLMFP), utilizes two specialized VLMs that work together to turn visual planning problems into ready-to-use files for formal planning software.

The researchers first carefully trained a small model they call SimVLM to specialize in describing the scenario in an image using natural language and simulating a sequence of actions in that scenario. Then a much larger model, which they call GenVLM, uses the description from SimVLM to generate a set of initial files in a formal planning language known as the Planning Domain Definition Language (PDDL).

The files are ready to be fed into a classical PDDL solver, which computes a step-by-step plan to solve the task. GenVLM compares the results of the solver with those of the simulator and iteratively refines the PDDL files.

“The generator and simulator work together to be able to reach the exact same result, which is an action simulation that achieves the goal,” Hao says.

Because GenVLM is a large generative AI model, it has seen many examples of PDDL during training and learned how this formal language can solve a wide range of problems. This existing knowledge enables the model to generate accurate PDDL files.

A flexible approach

VLMFP generates two separate PDDL files. The first is a domain file that defines the environment, valid actions, and domain rules. It also produces a problem file that defines the initial states and the goal of a particular problem at hand.

“One advantage of PDDL is the domain file is the same for all instances in that environment. This makes our framework good at generalizing to unseen instances under the same domain,” Hao explains.

To enable the system to generalize effectively, the researchers needed to carefully design just enough training data for SimVLM so the model learned to understand the problem and goal without memorizing patterns in the scenario. When tested, SimVLM successfully described the scenario, simulated actions, and detected if the goal was reached in about 85 percent of experiments.

Overall, the VLMFP framework achieved a success rate of about 60 percent on six 2D planning tasks and greater than 80 percent on two 3D tasks, including multirobot collaboration and robotic assembly. It also generated valid plans for more than 50 percent of scenarios it hadn’t seen before, far outpacing the baseline methods.

“Our framework can generalize when the rules change in different situations. This gives our system the flexibility to solve many types of visual-based planning problems,” Fan adds.

In the future, the researchers want to enable VLMFP to handle more complex scenarios and explore methods to identify and mitigate hallucinations by the VLMs.

“In the long term, generative AI models could act as agents and make use of the right tools to solve much more complicated problems. But what does it mean to have the right tools, and how do we incorporate those tools? There is still a long way to go, but by bringing visual-based planning into the picture, this work is an important piece of the puzzle,” Fan says.

This work was funded, in part, by the MIT-IBM Watson AI Lab.

Nature Climate Change, Published online: 11 March 2026; doi:10.1038/s41558-026-02578-0

The adoption and effectiveness of carbon pricing are highly reshaped by interactions with other climate mitigation policies. A global comparative assessment of policy synergies and conflicts can guide policymakers in designing policy portfolios that can achieve higher mitigation cost-effectiveness.

Nature Climate Change, Published online: 11 March 2026; doi:10.1038/s41558-026-02574-4

Interactions between climate policy instruments can have synergistic and conflicting effects, but these interactions are not systematically understood. This research provides global evidence on how policy characteristics and interactions in different contexts could lead to different outcomes.

Nature Climate Change, Published online: 11 March 2026; doi:10.1038/s41558-026-02562-8

The feasibility of climate policies hinges on public support. A survey of 13 EU countries shows that ‘middle groups’—citizens whose support across mitigation measures varies, rather than being uniformly supportive or opposed—play a pivotal role in shaping overall public policy support and electoral outcomes.

The government should not help a religious institution to punish or deter members from inquiring about their faith. Yet, once again, the Watch Tower Bible and Tract Society is trying to use flimsy copyright claims to exploit the special legal tools available to copyright owners in order to unmask anonymous online speakers. And, once again, EFF has stepped in to urge the courts not to give Watch Tower’s attempts the force of law, with the help of local counsel Jonathan Phillips of Phillips & Bathke, P.C.

EFF’s client, J. Doe, is a member of the Jehovah’s Witnesses who became interested in the history of the organization’s public statements, and how they’ve changed over time. They created research tools to analyze those documents and ultimately created a website, JWS Library, allowing others to use those tools and verify their findings through an archive that included documents suppressed by the church. Doe and others discovered prophecies that failed to come true, erasure of a leader’s disgrace, increased calls for obedience and donations, and other insights about the Jehovah’s Witnesses’ practices. Doe also used machine translation on a foreign-language document to help the community understand what the church was saying to different audiences and also to help understand potential changes in the organization’s attitudes towards dissent.

Within the church, dissent or even asking questions has often been punished by labeling members as apostates and ostracizing—or “disfellowshipping”— them. As a result, Doe and others choose to speak anonymously to avoid retaliation that could cost them family, friend, and professional relationships.

There is no law against questioning the Jehovah’s Witnesses. Instead, Watch Tower argues that Doe’s activities constitute copyright infringement and seeks to use the special process provided in the Digital Millennium Copyright Act (DMCA) to unmask them. It sent DMCA subpoenas to Google and Cloudflare, seeking information that would help them uncover Doe’s identity.

The problem for Watch Tower is that Doe’s research and commentary are clear fair uses allowed under copyright law. The First Amendment does not permit the unmasking of anonymous speakers based on such weak claims. Indeed, the First Amendment protects anonymous speakers precisely because some would be deterred from speaking if they faced retribution for doing so.

EFF stands with those who question the claims of those in power and who share the tools and knowledge needed to do so. We urge the judges in the Southern District of New York to quash these improper subpoenas and not allow copyright to be used to suppress important, legitimate speech.