Some might say this is the best era for artificial intelligence. Some might say there’s never been a better time to get a clearer understanding of the various forms of AI, its definitions, and applications. Many IT professionals involved in AI development have a strong technical understanding. However, some faculty, administrators, and students lack a clear understanding of this technology and how it will change their education and future lives. We hope to provide a basic understanding of current AI terminology and provide insight into its many applications in different fields and situations.
Generation AI
The origins of early generative AI can be traced back to the 1960s with the introduction of chatbots. MIT scientist Joseph Weizenbaum designed the first computer program called ELIZA. This program is designed to allow you to have natural conversations with humans through psychotherapy-like interactions. Even then, writer Brian Merchant wrote, “Weizenbaum recognized that the human desire for relationships and the ease with which computer programs can be built and sold to prey on that desire made people vulnerable to mass exploitation at the hands of those who owned and operated computer systems.” More than 60 years ago, there were already concerns about the power of AI to dehumanize our society.
In late 2022, generative AI became even more mainstream with the ability to scan large amounts of data through machine learning models and create new content through predictions. In November 2022, OpenAI made ChatGPT publicly available and millions of consumers used it. Numerous generative AI tools can now create original text, code, and media to create recommendations and hypotheses that benefit business and education. Businesses and institutions can improve customer experiences through virtual agents, and STEM scientists and researchers can use AI models to solve complex science and research problems. However, generative AI initially had some drawbacks. Some models were trained using data created or collected with cultural biases, others were factually incorrect or false, and there were data privacy concerns as well as copyright violations. As these models have matured and evolved, their output has improved and updated versions or similar models have been introduced, such as Google’s Gemini, DeepSeek, and most recently Anthropic’s Claude.
agent AI
More recently, 2024 was the year that traditional chatbot technology and basic large-scale language models (LLMs) transitioned to agent AI and became more mainstream. One important aspect of this technology is the ability to complete tasks autonomously. Quiq, an AI-powered messaging platform, explains on its website: “Agent platforms typically use LLM as a foundation, but add layers of autonomy, reasoning, and action-taking capabilities. This difference is important. The basic LLM answers questions, and agent AI solves problems.” This ability to be autonomous in education has the potential to provide more personalized student support, create modern curriculum and automated grading processes, and enhance the research and lab experience.
physical AI
Another technology is physical AI. This refers to machines that can interact with their physical environment and perceive the world in real time through sensors, video cameras, and audio equipment. As a technical publication CNET “Generative AI models, such as ChatGPT, predict patterns in text, images, and audio. Physical AI models must predict outcomes in dynamic, real-world environments,” we noted in March. High-end robotics shows the power of physical AI. Examples include robotaxis from Uber and Waymo, Amazon’s autonomous warehouse logistics, robotic surgical instruments like the da Vinci Surgical System, Boston Dynamics’ sophisticated robots, and John Deere tractors in agriculture that leverage autonomous navigation. While some experts write about the potential for AI to dehumanize society, others point to the concept of collaborative robots (cobots), where robots and humans work together to perform different tasks for which each is best suited. Writing on the industrial automation company Taboron’s website, Robert Komlienovich, president of automation integration, explained both the pros and cons of cobots and emphasized the importance of “carefully considering the cons before investing” in this technology.
Deploying AI in higher education
Much has been written about the impact of AI in our daily lives, work, and education. Constant innovation and new definitions are hard to keep up with. For educators, there are a set of key AI concepts to help universities navigate this ever-changing landscape. First, higher education must continue to work diligently to ensure the integrity and credibility of academic research and to recognize copied or false data and information.
Universities should have protocols in place to minimize or eliminate bias and ensure equal access to AI tools and applications. Additionally, strong privacy and data protection policies are required for the entire ecosystem, including faculty, staff, students, and the campus community.
Effective use of generative, agentic, and physical AI tools requires proper training and development for all end users. This training should not be considered a “one and done” experience, but rather an ongoing process that requires annual updates and revisions. Beyond this training, we need to make sure that we properly educate and prepare students about how these AI tools work, their potential, and their dangers. Informing and practicing good AI ethics to campus communities is essential in all disciplines and settings. Mark McCormack, Senior Director of Research and Insights at EDUCAUSE, said earlier this year that “2026 “In 2020, technology leaders will focus on equipping and empowering people across their institutions to realize the ultimate benefits of technology, AI, and data. This will require leaders who can educate and train users to safely and effectively deploy these tools, while also working closely with academic and program leaders to ensure students gain the skills they need for their educational journeys and future careers.” We have a lot of work to do as educators and engineers. Continuing clarity on the changing landscape of AI will help us successfully navigate the future of education and the profession.
