Artificial Intelligence, or AI, is a field of computer science that focuses on developing algorithms and systems that can learn from data, recognise patterns, and make decisions or predictions based on those algorithms or systems.
Examples of AI applications in everyday life include chatbots, image or voice recognition software, autonomous vehicles, and recommendation systems (for films, music or online shopping).
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Here’s a summary of how AI is being used in Higher Education (HE), with a focus on libraries:
General Uses in HE:
Study support: AI research assistants and chatbots help to answer questions and explain concepts.
Writing help: AI tools assist with grammar, spelling and structure.
Time management: Smart planners and reminders help students organise study schedules.
Accessibility: AI tools support students with additional learning needs (e.g. speech-to-text, screen readers)
In Libraries:
Research support: AI tools assist with data analysis, literature reviews, and writing.
Smarter searching: AI tools help make finding books, articles, and resources easier.
24/7 help: Chatbots and AI-enhanced search engines answer help users find answers to their questions any time.
Resource recommendations: AI suggests useful keywords, search prompts or even reading material based on your course or interests.
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Academic Integrity: Acting in an honest, ethical, and transparent manner in an academic setting.
Accuracy (of outputs): How correct, precise, and relevant is the information generated by the AI tool.
Algorithm: A set of rules or procedures followed by a computer to solve a particular problem or perform a task.
Artificial Intelligence (AI): Computer systems designed to perform tasks that usually require human intelligence, such as learning, problem-solving, and decision-making.
Bias: Prejudice for or against something, a person, or group compared with another, usually in a way considered to be unfair
Comprehensiveness (of literature coverage): How thoroughly is the existing research on a topic being covered.
Collaborative: Working with others towards a common goal.
Currency: Relates to the timeliness of the resources or refers to how recent the information is.
Generative AI: a type of AI that can make new text, pictures, or data using special models. These models use prompts to learn from examples and then they create similar content from that existing data.
Hallucinations: Refers to erroneous ("fake") AI-generated outputs.
Interrogation (of results): Critically examining and questioning the outcomes or findings in the outputs from an AI tool.
Large Language Model: A model that uses massive amounts of training data to teach algorithms without human instruction. ChatGPT is an example of this.
Machine Learning (ML): A subset of AI that involves systems learning from data and improving their performance over time without explicit programming.
Output: Any content generated by AI.
Prompt: A specific input or instruction given to a generative model to generate output.
Synthesise: Combining different ideas or information from a number of sources into a coherent whole.
Training Data: Training data refers to the set of examples or information used to teach a machine learning model how to perform a specific task.
