AI in Education Harvard Graduate School of Education
Participants also spent a major portion of the day engaged in small discussion groups in which faculty, students, researchers, staff, and other guests shared their ideas about AI in education. So again, here’s something that people aren’t talking about where AI on the front end can create rich evocative situations, and AI and machine learning on the back end can find really interesting patterns for improvement. First, privacy and transparency have been ongoing and underaddressed concerns for educational systems across all levels. Domain experts need to work with government to create sound policy to protect and enable young people in school. Second, while it’s not yet well understood what impact these technologies might have on work opportunities for both young people and adults now and in the future, that impact could potentially be great. This area of concern has significant implications for any changes to educational systems.
He has spent decades exploring emerging learning technologies as a Harvard researcher. The recent explosion of generative AI, like ChatGPT, has been met with mixed reactions in education. Some colleges and universities have tweaked their teaching and learning already. Academic assessment approaches must evolve beyond isolated assignments toward more continuous, data-driven views. Combining multiple formative and summative approaches continues to offer an enduring path forward. In parallel, leveraging generative AI tools to streamline productivity and create credible first drafts of content or enhance conversational user interfaces to better support students will likely combine to support improved educational experiences.
Areas of Focus
If people are really going to start to use AI to carry out high-stakes operations, then we’re going to need ways to detect and correct for these biases, even if just as a matter of making AI tools commercially viable. Then we need to address all these other forms of injustice that can not only exist on the surface, but also can be perpetuated with the way we use language. All the -isms — racism Yakov Livshits and sexism for example — often start off as linguistic habits that we carry over into other kinds of actions. If they’re present in the language and the AI training set of data, they’re going to be present in the output of the AI model. Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks.
UNSW colleagues can access internal resources, including UNSW’s Guiding Principles about Generative AI in teaching and assessment, on a dedicated curated intranet site. The full impact of AI in education remains unclear at this juncture, but as all speakers agreed, things are changing, and now is the time to get it right. Now, no one has done a study, I assume, of flooding in Des Moines, Iowa, in 2050 based on mid-level projections about climate change. Familiarise yourself with UCL’s guidance for students on Engaging with AI in Your Education. Option 2 (slides 15-16) suggests a discussion on ethical usage of GenAI using mentimeter or mural.
Bibliographic and Citation Tools
You are welcome to use this guide if you are from another educational facility, but you must credit UCL. UCL has opted to promote ethical and transparent engagement with GenAI tools rather than seek to ban them. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
This has led to a more general debate about responsible AI and whether restrictions should be put in place to prevent data scientists from scraping the internet to get the large data sets required to train their generative models. Some people are concerned Yakov Livshits about the ethics of using generative AI technologies, especially those technologies that simulate human creativity. AI Dungeon – this online adventure game uses a generative language model to create unique storylines based on player choices.
He’s happy with how the tool saves him time and lets him feel more present and less preoccupied at his daughter’s sporting events, but also with how he can quickly generate content that gives students a sense of belonging. Education is rapidly evolving, and generative AI is likely to play a major role in shaping its future. We can anticipate seeing cutting-edge generative AI applications in education as the technology develops. For instance, generative AI could be used to create virtual teachers that would provide students with 24/7 support, allowing them to learn at their own pace and on their own schedule. This could be particularly beneficial for students in remote or underserved areas who may not have access to traditional classroom-based education. Generative AI essentially produces content (e.g., text, images, audio, video) for the digital publishing industry in education, which can have huge cost reduction benefits.
- Joseph South, chief learning officer at the International Society for Technology in Education (ISTE), whose backers include Meta and Walmart, says educators are used to gritting their teeth and waiting for the latest education technology fad to pass.
- For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.
- And in turn, you can focus much more deeply on personalization to individual students, on bringing in cultural dimensions and equity dimensions that AI does not understand and cannot possibly help with.
- However, many institutions now have policies to control and restrict inappropriate student and staff use and faculty members who encourage appropriate student exploration and evaluation.
- Several themes emerged over the course of the day on AI’s potential, as well as its significant risks.
- As with almost all new technology, however, they raise risks and challenges.
GenAI is a recent example of neural networks trained on massive datasets to generate new content from simple prompts. Large language models (LLMs) such as ChatGPT (Open AI), Bard (Google), or Claude (Anthropic) can generate text in multiple languages and styles. Other generative programs generate images, video, audio, and code from text instructions.
Title:Generative AI: Implications and Applications for Education
Once a generative AI algorithm has been trained, it can produce new outputs that are similar to the data it was trained on. Because generative AI requires more processing power than discriminative AI, it can be more expensive to implement. A simplified version of the writing process that my colleagues and I teach students is write, review, revise and then repeat. It’s how we become better writers, which is what we’re often more concerned with in a writing course — helping the writers, not just the writing, improve! Integrating AI into higher education is not a futuristic vision but an inevitability. Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring.