How artificial intelligence is changing higher education

With the rise of generative artificial intelligence, students must acquire new skills, while educators must embrace new teaching methods.

Because artificial intelligence is likely to become the primary means of accessing relevant data, professors must prepare students to use the technology effectively in their lives and careers. Students will need to develop skills related to effective prompt engineering, which is the ability to create questions that elicit the most useful responses from AI platforms. The more comfortable faculty members become with AI, the better they will be at teaching students how to use it ethically and effectively in the coming years. While ChatGPT has acquired considerable prominence, artificial intelligence (AI) is not new. AI technology has existed since the mid-20th century. However, the fact that it is now being used in more applications and is more accessible to the public has altered the conversation, as we recognize that the technology will have a significant impact on 80% of jobs, including teaching.


Within the coming years, Ai systems will most likely be the primary way we access knowledge—and higher education institutions will be where many people learn how to use the technology efficiently. This new reality makes it clear that it’s time for educators to master a new skill set relevant to AI and to teach those skills to their students. Although many students are tinkering with AI models such as ChatGPT, the first place they should learn how to use AI effectively is in the classroom. However, this will only happen if schools have integrated relevant learning activities into their curricula and linked those activities to appropriate educational objectives. However, because many faculty are uncomfortable with AI, they will need to learn how to implement it into their classrooms. Schools can begin by showing students and faculty that AI can be viewed as an ally rather than an adversary.


To convey this, classrooms can perform several tasks to educate students and staff to technology: Create seminars to train faculty on how to introduce use cases of AI and highlight the benefits and limitations of this technology. Use learning experience platforms (LXPs) that leverage adaptive learning and AI processes that allow faculty to detect when students are struggling with the material and to customize their courses. Faculty can use feedback from an LXP to help students gain a deeper understanding of their weaknesses and to improve academic outcomes. Use RPA systems to automate repetitive tasks, such as students submitting assignments to correctors. Once faculty are familiar with the technology, the the most important skill they will need to teach is the art of prompt engineering—the ability to formulate questions that evoke the most helpful feedback.  Effective prompt engineering contains the following areas in which both faculty and students will need to improve their knowledge and skills: Large Language Models (LLMs). Understanding LLMs—the algorithms that power generative AI output—is critical to unlocking ChatGPT’s full potential and tailoring interactions with it. Users can only elicit desired responses from the language model by carefully crafting prompts that are clear and concise. This makes it vital for faculty to incorporate operating systems like ChatGPT into their classrooms. Then they can teach students how to direct ChatGPT’s output toward their desired outcomes. Students’ goal is to interact with ChatGPT as if they were having a conversation with a human. To accomplish this, they must first understand how LLMs function. Communication. Students may regard effective communication as less important when entering a prompt into ChatGPT than when interacting with their professors or peers. However, providing clear and explicit instructions in an AI prompt is just as important as providing clear instructions to colleagues—good communication streamlines users’ interactions with AI. Faculty can teach students how to improve their inquiries in order to get more specific answers. They will learn how to create context, describe a task, and specify the desired output for the language model. Students’ goal is to communicate with ChatGPT as if they were having a discussion with a human.


Students who are better at giving AI demands are more likely to have seamless learning experiences as they can use AI to expand their comprehension of complicated concepts, solve issues, and explore new areas of knowledge. Students will use this skill to give relevant elements and background information within the prompt, allowing the language model to comprehend the accurate context of each query. They boost their probability of gaining accurate and meaningful responses by setting the stage and providing all necessary details. Rather than forbidding the use of AI in the classroom, how could professors endorse it? They can implement evolving instructional models that use AI to build abilities in a variety of areas: Assessment. The emergence of AI will prompt a revision and rethinking of education and evaluation methodologies. Faculty can use AI platforms to differentiate student assessments, such as formative, normative, and ipsative evaluations. They can even utilize AI to examine a new set of AI-related talents. These abilities include the development of analytical mindsets, experience with intelligent user interfaces, the capacity to look beyond statistics and instead apply predictive analytics, and knowledge of artificial neural networks. Faculty will inevitably collaborate with AI models to support their instruction. As a result, higher education institutions must help faculty as they tackle this new challenge and do everything possible to ensure that collaboration is successful. In essence, university faculty act as gardeners, nourishing two separate seeds in rich ground. They are developing AI-ready pupils while also guiding the future application of AI technologies. Faculty may not be able to halt the AI trend, but in this capacity, they may direct it by teaching students to use it as effectively and responsibly as feasible. In the upcoming decades, it will be fascinating and enriching to witness how faculty teach AI models, as well as how students decide to employ AI models to solve issues in class. Technology could also assist us solve one of the more difficult parts of teaching: how to interest pupils in their learning process. Students will need to master the competencies outlined above to effectively work with AI. In fact, we anticipate that as teachers and students continue to experiment with AI models, they will learn to see the technology as a colleague rather than a danger.

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