Inside OpenAI's plan to embed ChatGPT into college students' lives
OpenAI, the maker of ChatGPT, has a plan to overhaul college education — by embedding its artificial intelligence tools in every facet of campus life.
If the company's strategy succeeds, universities would give students AI assistants to help guide and tutor them from orientation day through graduation. Professors would provide customized AI study bots for each class. Career services would offer recruiter chatbots for students to practice job interviews. And undergrads could turn on a chatbot's voice mode to be quizzed aloud before a test.
OpenAI dubs its sales pitch 'AI-native universities.'
'Our vision is that, over time, AI would become part of the core infrastructure of higher education,' Leah Belsky, OpenAI's vice president of education, said in an interview. In the same way that colleges give students school email accounts, she said, soon 'every student who comes to campus would have access to their personalized AI account.'
To spread chatbots on campuses, OpenAI is selling premium AI services to universities for faculty and student use. It is also running marketing campaigns aimed at getting students who have never used chatbots to try ChatGPT.
Some universities, including the University of Maryland and California State University, are already working to make AI tools part of students' everyday experiences. In early June, Duke University began offering unlimited ChatGPT access to students, faculty and staff. The school also introduced a university platform, called DukeGPT, with AI tools developed by Duke.
OpenAI's campaign is part of an escalating AI arms race among tech giants to win over universities and students with their chatbots. The company is following in the footsteps of rivals like Google and Microsoft that have for years pushed to get their computers and software into schools, and court students as future customers.
The competition is so heated that Sam Altman, OpenAI's CEO, and Elon Musk, who founded the rival xAI, posted dueling announcements on social media this spring offering free premium AI services for college students during exam period. Then Google upped the ante, announcing free student access to its premium chatbot service 'through finals 2026.'
OpenAI ignited the recent AI education trend. In late 2022, the company's rollout of ChatGPT, which can produce human-sounding essays and term papers, helped set off a wave of chatbot-fueled cheating. Generative AI tools like ChatGPT, which are trained on large databases of texts, also make stuff up, which can mislead students.
Less than three years later, millions of college students regularly use AI chatbots as research, writing, computer programming and idea-generating aides. Now OpenAI is capitalizing on ChatGPT's popularity to promote the company's AI services to universities as the new infrastructure for college education.
OpenAI's service for universities, ChatGPT Edu, offers more features, including certain privacy protections, than the company's free chatbot. ChatGPT Edu also enables faculty and staff to create custom chatbots for university use. (OpenAI offers consumers premium versions of its chatbot for a monthly fee.)
OpenAI's push to AI-ify college education amounts to a national experiment on millions of students. The use of these chatbots in schools is so new that their potential long-term educational benefits, and possible side effects, are not yet established.
A few early studies have found that outsourcing tasks like research and writing to chatbots can diminish skills like critical thinking. And some critics argue that colleges going all-in on chatbots are glossing over issues like societal risks, AI labor exploitation and environmental costs.
OpenAI's campus marketing effort comes as unemployment has increased among recent college graduates — particularly in fields like software engineering, where AI is now automating some tasks previously done by humans. In hopes of boosting students' career prospects, some universities are racing to provide AI tools and training.
California State University announced this year that it was making ChatGPT available to more than 460,000 students across its 23 campuses to help prepare them for 'California's future AI-driven economy.' Cal State said the effort would help make the school 'the nation's first and largest AI-empowered university system.'
Some universities say they are embracing the new AI tools in part because they want their schools to help guide, and develop guardrails for, the technologies.
'You're worried about the ecological concerns. You're worried about misinformation and bias,' Edmund Clark, the chief information officer of California State University, said at a recent education conference in San Diego. 'Well, join in. Help us shape the future.'
Last spring, OpenAI introduced ChatGPT Edu, its first product for universities, which offers access to the company's latest AI. Paying clients like universities also get more privacy: OpenAI says it does not use the information that students, faculty and administrators enter into ChatGPT Edu to train its AI.
(The New York Times has sued OpenAI and its partner, Microsoft, over copyright infringement. Both companies have denied wrongdoing.)
Last fall, OpenAI hired Belsky to oversee its education efforts. An ed tech startup veteran, she previously worked at Coursera, which offers college and professional training courses.
She is pursuing a two-pronged strategy: marketing OpenAI's premium services to universities for a fee while advertising free ChatGPT directly to students. OpenAI also convened a panel of college students recently to help get their peers to start using the tech.
Among those students are power users like Delphine Tai-Beauchamp, a computer science major at the University of California, Irvine. She has used the chatbot to explain complicated course concepts, as well as help explain coding errors and make charts diagraming the connections between ideas.
'I wouldn't recommend students use AI to avoid the hard parts of learning,' Tai-Beauchamp said. She did recommend students try AI as a study aid. 'Ask it to explain something five different ways.'
Belsky said these kinds of suggestions helped the company create its first billboard campaign aimed at college students.
'Can you quiz me on the muscles of the leg?' asked one ChatGPT billboard, posted this spring in Chicago. 'Give me a guide for mastering this Calc 101 syllabus,' another said.
Belsky said OpenAI had also begun funding research into the educational effects of its chatbots.
'The challenge is, how do you actually identify what are the use cases for AI in the university that are most impactful?' Belsky said during a December AI event at Cornell Tech in New York City. 'And then how do you replicate those best practices across the ecosystem?'
Some faculty members have already built custom chatbots for their students by uploading course materials like their lecture notes, slides, videos and quizzes into ChatGPT.
Jared DeForest, the chair of environmental and plant biology at Ohio University, created his own tutoring bot, called SoilSage, which can answer students' questions based on his published research papers and science knowledge. Limiting the chatbot to trusted information sources has improved its accuracy, he said.
'The curated chatbot allows me to control the information in there to get the product that I want at the college level,' DeForest said.
But even when trained on specific course materials, AI can make mistakes. In a new study — 'Can AI Hold Office Hours?' — law school professors uploaded a patent law casebook into AI models from OpenAI, Google and Anthropic. Then they asked dozens of patent law questions based on the casebook and found that all three AI chatbots made 'significant' legal errors that could be 'harmful for learning.'
'This is a good way to lead students astray,' said Jonathan S. Masur, a professor at the University of Chicago Law School and a co-author of the study. 'So I think that everyone needs to take a little bit of a deep breath and slow down.'
OpenAI said the 250,000-word casebook used for the study was more than twice the length of text that its GPT-4o model can process at once. Anthropic said the study had limited usefulness because it did not compare the AI with human performance. Google said its model accuracy had improved since the study was conducted.
Belsky said a new 'memory' feature, which retains and can refer to previous interactions with a user, would help ChatGPT tailor its responses to students over time and make the AI 'more valuable as you grow and learn.'
Privacy experts warn that this kind of tracking feature raises concerns about long-term tech company surveillance.
In the same way that many students today convert their school-issued Gmail accounts into personal accounts when they graduate, Belsky envisions graduating students bringing their AI chatbots into their workplaces and using them for life.
'It would be their gateway to learning — and career life thereafter,' Belsky said.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
&w=3840&q=100)
First Post
38 minutes ago
- First Post
Vijay Mallya talks about choosing Katrina Kaif and Deepika Padukone as Kingfisher models, tells Raj Shamani: 'We chose the right girls, had all the heroines & stars and...'
The business tycoon said, 'We chose the right girls, whether it was Deepika Padukone, Katrina Kaif. We had all the heroines and the stars on the calendar at a younger age. We picked the right talent.' read more On his latest podcast with Raj Shamani, Vijay Mallya talked about choosing Katrina Kaif and Deepika Padukone as Kingfisher models. The business tycoon said, 'We chose the right girls, whether it was Deepika Padukone, Katrina Kaif. We had all the heroines and the stars on the calendar at a younger age. We picked the right talent.' Mallya added, 'I did it because it was a fantastic marketing tool. Did not bring anything to me personally. But it did wonders to the brand,' he said. Addressing speculation about his personal ties to the actresses, Mallya clarified, 'It was strictly to the calendar.' STORY CONTINUES BELOW THIS AD Mallya, who is accused of defaulting loans worth over Rs 9,000 crore and is currently fighting New Delhi's request for extradition in the United Kingdom, issued a rare apology for the failure of Kingfisher Airlines, denied accusations of theft and offered his take on why he left India nearly a decade back and has been on the run ever since during a four-hour conversation with popular YouTuber Raj Shamani. 'I was very impressed with the pitch that Lalit Modi made to the BCCI committee about this league. He called me one day and said ok, teams are going to be auctioned. Are you going to buy it? So, I bid from three franchises, and I lost Mumbai by a very small amount of money,' Mallya said on the latest episode of Figuring Out With Raj Shamani that was posted to the Google-owned video-sharing platform on Thursday. Mallya, who was a regular at RCB's matches along with his son Siddharth before leaving India, added that he wanted the franchise – which enjoys a huge fan following and a brand value that rivals that of five-time champions Chennai Super Kings and Mumbai Indians –to be 'more than a cricket team'.
News18
an hour ago
- News18
Energy And Electrons: Could They Become Currency In A Decade? What Nikhil Kamath Says
Last Updated: Zerodha co-founder Nikhil Kamath predicts energy and electrons could become trade currency in a decade due to rising data center and AI energy consumption. Zerodha co-founder Nikhil Kamath has shared a thought-provoking idea based on research with a lingering assumption that energy and electrons might be the currency of trade in a decade. In a series of tweets, he shared the infographics explaining the research, highlighting the growing financial footprint of data centers and artificial intelligence. The electricity consumption in data centers is insane, with one new Data center consuming more electricity than 4 lakh electric vehicles in a year. Thus, the research says, electricity alone eats up 65 per cent of a data center's costs – primarily for computing and cooling. Data centers are large facilities that store, process, and manage digital data. They are the backbone of the internet and digital services—every time you use a website, stream a video, store files in the cloud, or make a bank transaction, chances are it passes through a data center. US is the leader with the most number of data centers (3680), followed by Germany (424) and UK (418). India ranks seventh in the number of 262 data centers. The more servers the data center has, the more energy it requires. Earlier, OpenAI founder Sam Altman explained how the words like please and thank you cost the company tens of millions of dollars. The research adds that one ChatGPT query uses 10x the electricity of a regular Google search. Data Centers To Consume 10% Of Global Energy By 2030 The research shows that data centers' consumption of energy is expected to grow to 10 per cent by 2030, which is 1.5 per cent of global energy. 'Just 5% of global internet searches using AI could consume enough energy to power over 1 million Indian homes for a year." Energy To Become Asset Kamath's idea is rooted in an emerging economic paradigm: as the demand for electricity grows, energy becomes a critical and tradable asset—much like money. If energy is as essential as cash in powering AI, streaming services, financial systems, and cloud operations, pricing and trading electricity akin to currency could be the next step. This concept isn't just futuristic—it carries real-world relevance. Companies might begin to hedge not only currency risk but also energy risk. Imagine supermarkets trading kilowatt-hours or data centers using energy derivatives in a similar fashion to forex trading. Over time, energy tokens or blockchain-based credits for electrons could emerge, giving rise to a sophisticated energy-backed financial ecosystem. If this shift happens, economies would have to rethink everything from inflation metrics to banking, as 'energy currency" could fundamentally alter how value is transferred and stored—making energy both a means and a measure of wealth. About the Author Varun Yadav First Published: June 08, 2025, 15:03 IST News business Energy And Electrons: Could They Become Currency In A Decade? What Nikhil Kamath Says
The Hindu
an hour ago
- The Hindu
Physics changed AI in the 20th century. Is AI returning the favour now?
Artificial intelligence (AI) is booming. Various AI algorithms are used in many scientific domains, such as to predict the structure of proteins, search for materials with particular properties, and interpret medical data to provide a diagnosis. People use tools like ChatGPT, Claude, NotebookLM, DALL-E, Gemini, and Midjourney to generate images and videos from text prompts, write text, and search the web. The question arises in the same vein: can they prove useful in studies of the fundamental properties of nature or is there a gap between human and artificial scientists that needs to be bridged first? There is certainly some gap. Many of the current applications of AI in scientific research often use AI models as a black box: when the models are trained on some data and they produce an output, but the relationship between the inputs and the output is not clear. This is considered unacceptable by the scientific community. Last year, for example, DeepMind faced pressure from the life sciences community to release an inspectable version of its AlphaFold model that predicts protein structures. The black-box nature presents a similar concern in the physical sciences, where the steps leading up to a solution are as important as the solution itself. Yet this hasn't dissuaded scientists from trying. In fact, they started early: since the mid-1980s, they have integrated AI-based tools in the study of complex systems. In 1990, high-energy physics joined the fold. Astro- and high-energy physics In astronomy and astrophysics, scientists study the structure and dynamics of celestial objects. Big-Data analytics and image enhancement are two major tasks for researchers in this field. AI-based algorithms help with the first by looking for patterns, anomalies, and correlations. Indeed, AI has revolutionised astrophysical observations by automating tasks like capturing images and tracking distant stars and galaxies. AI algorithms are able to compensate for the earth's rotation and atmospheric disturbances, producing better observations in a shorter span. They are also able to 'automate' telescopes that are looking for very short-lived events in the sky and record important information in real time. Experimental high-energy physicists often deal with large datasets. For example, the Large Hadron Collider experiment in Europe generates more than 30 petabytes of data every year. A detector on the collider called the Compact Muon Solenoid alone captures 40 million 3D images of particle collisions every second. It is very difficult for physicists to analyse such data volumes rapidly enough to track subatomic events of interest. So in one measure, researchers at the collider started using an AI model able to accurately identify a particle of interest in very noisy data. Such a model helped discover the Higgs boson particle over a decade ago. AI in statistical physics Statistical mechanics is the study of how a group of particles behaves together, rather than individually. It is used to understand macroscopic properties like temperature, and pressure. For example, Ising developed a statistical model for magnetism in the 1920s, focusing on the collective behavior of atomic spins interacting with their neighbors. In this model, there are higher and lower energy states for the system, and the material is more likely to exist in the lowest energy state. The Boltzmann distribution is an important concept in statistical mechanics, used to predict, say, the precise conditions in which ice will turn to water. Using this distribution, in the 1920s, Ernst Ising and Wilhelm Lenz predicted the temperature at which a material changed to non-magnetic from magnetic. Last year's physics Nobel laureates John Hopefield and Geoffrey Hinton developed a theory of neural networks in the same way, based on the idea of statistical mechanics. An NN is a type of model where nodes that can receive data to perform computations on them are linked to each other in different ways. Overall, NNs process information the way animal brains do. For example, imagine an image made up of pixels, where some are visible and the rest are hidden. To determine what the image is, physicists have to consider all possible ways the hidden pixels could fit together with the visible pieces. The idea of most likely states of statistical mechanics could help them in this scenario. Hopefield and Hinton developed a theory for NNs that considered the collective interactions of pixels as neurons, just like Lenz and Ising before them. A Hopfield network calculates the energy of an image by determining the least-energy arrangement of hidden pixels, similar to statistical physics. AI tools apparently returned the favour by helping make advances in the study of Bose-Einstein condensates (BEC). A BEC is a peculiar state of matter that a collection of certain subatomic or atomic particles have been known to enter at very low temperatures. Scientists have been creating it in the lab since the early 1990s. In 2016, scientists at Australian National University tried to do so using AI's help with creating the right conditions for a BEC to form. They found that it did so with flying colours. The tool was even able to help keep the conditions stable, allowing the BEC to last longer. 'I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour,' the paper's coauthor Paul Wigley said in a statement. 'A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out.' Bringing AI to the quantum In a 2022 paper, scientists from Australia, Canada, and Germany reported a simpler method to entangle two subatomic particles using AI. Quantum computing and quantum technologies are of great research and practical interest today, with governments — including India's — investing millions of dollars in developing these futuristic technologies. A big part of their revolutionary power comes from achieving quantum entanglement. For example, quantum computers have a process called entanglement swapping: where two particles that have never interacted become entangled using intermediate entangled particles. In the 2022 paper, the scientists reported a tool called PyTheus, 'a highly-efficient, open-source digital discovery framework … which can employ a wide range of experimental devices from modern quantum labs' to better achieve entanglement in quantum-optic experiments. Among other results, scientists have used PyTheus to make a breakthrough with implications for quantum networks used to securely transmit messages, making these technologies more feasible. More work, including research, remains to be done but tools like PyTheus have demonstrated a potential to make it more efficient. From this vantage point in time, it seems like every subfield of physics will soon use AI and ML to help solve their toughest problems. The end goal is to make it easier to come up with the more appropriate questions, test hypotheses faster, and understand results more gainfully. The next groundbreaking discovery may well come from collaborations between human creativity and machine power. Shamim Haque Mondal is a researcher in the Physics Division, State Forensic Science Laboratory, Kolkata.
