AI tools may not know about recent events or research. They do not sweep the internet everyday, their outputs are based on data which could be from years ago. For example, at the time of writing, ChatGPT has limited knowledge about what’s happened in the world since 2021.
For all these reasons, you need to be careful when using AI – you still need to use the critical thinking abilities of your human brain! To help you navigate Artificial Intelligence, here are some do’s and don'ts:
When talking to AI tools, use short, simple sentences and precise, specific words to describe exactly what you want, including the format you want the response to be in. If it doesn’t understand your question, try phrasing it a different way with different words.
Never use AI to write essays for you. Learning how to research thoroughly, think critically and write persuasively are key skills you can gain from your course. You will not gain these skills if you get an AI to do the work for you. You risk being suspended from your course if you submit AI-generated work without explicitly stating that it is AI-generated. Assessed work is monitored for the use of Artificial Intelligence.
Do not use AI tools to generate bibliographies. It will generate completely fictitious books and articles. Remember that the AI is generating what it thinks sound like relevant citations, it is not checking whether those citations actually exist. Even if the citations it generates exist, the references may not be formatted correctly or include all of the necessary information.
Never rely on AI for research. Use multiple sources to make sure that your discoveries and insights have reliable foundations. We have over 100 million authoritative articles, books and other items in the Online Library. Our Summon search engine is an excellent research tool which searches across the databases we subscribe to.
Before getting students to use AI, it is important to understand their attitude and knowledge regarding this technology. Such insights can be gathered via a pre-course survey asking students what they think about AI generative tools, what they use AI for, and if they think it is useful to integrate the tool into classroom.
Lance Eaton, writer, educator, instructional designer at College Unbound and his team launched an anonymous student survey to study the use of ChatGPT, while involving students in developing AI usage policy. They also thought about creating a credit course in which students can actively explore ChatGPT, and other AI tools.
Adjusting classroom policies is considered among the top priorities of institutions when embracing AI technology. According to Dr. John FitzGibbon, Associate Director for Digital Learning Innovation in CDIL at Boston College, having clear policies regarding AI use brings a layer of transparency and honesty to students. They will be well aware of the specific tasks where AI is helpful or unhelpful; when using AI means cheating; how citation works, and more.
That is, instructors need to first help students identify the capacities and restrictions of AI tools: what they can and can not do. Dr. Fitzgibbon shares a great example of how he involved his students in discovering AI capacities. For each mid-term test question, he provided students with 4 answers (one of which was generated with ChatGPT) and asked them to choose which one is the best. Most students went for the AI-generated option since it was well written with sufficient reasoning and information. It was then he pointed out that this answer is actually bad, due to lack of proper referencing or weak reasoning. By doing this, students are immediately aware of AI limitations and more critical of the AI-generated content. Dr. Fitzgibbon concluded:
“Just be transparent with students about AI. Here it can be an expert for these reasons. It's not good here. And then here's how my expectations of your work has changed. Or, like, here's how you can use ChatGPT in your own work in this course. I've been very clear with students about how ChatGPT should be used, its limitations, its positives, etc.”
Once universal policies and expectations have been established and clearly communicated, students are encouraged to utilize AI tools in content generation processes. At this stage, students are free to explore the capacities of AI in different activities with the help of instructors. But first, they need to learn to produce quality prompts for AI generative tools.
It is true that AI generative tools like ChatGPT can produce anything, from an academic essay, curriculum vitae, to coding script, promotional materials, speeches, short stories, and much more. However, we can’t just simply provide basic prompts then expect AI to deliver well-written products. Getting good writing out of AI requires curation of specific and elaborate prompts, and this should be emphasized and highlighted for students at the beginning.
“Try asking for it to be concise or wordy or detailed, or ask it to be specific or to give examples. Ask it to write in a tone (ominous, academic, straightforward) or to a particular audience (professional, student) or in the style of a particular author or publication (New York Times, tabloid news, academic journal).” – Use ChatGPT to boost your writing
Providing thorough instructions is important, but it is also crucial to let students explore generative AI themselves. Therefore, instructors should make it clear that students don’t have to strictly follow the guidance and have freedom to generate prompts in their own ways. By navigating the use of AI tools, students gradually grasp the “language that ChatGPT is using”, according to Dr. Mollick.
It is easy for AI to create hallucinations or plausible facts, which are completely false content that look convincing. In other words, AI-generated content can be unreliable and students need to establish the ability to critically evaluate these responses.
After students generate content using AI, they should be asked to critically analyze these drafts and check every fact and claim mentioned. This can be done either individually or collaboratively. To make the evaluation process more fruitful, instructors should provide students with a rubric outlining the criteria when analyzing AI-generated content. University of North Carolina outlined a comprehensive set of evaluative criteria, namely:
This self-evaluative step can be turned into a peer or group assessment activity, resulting in meaningful dialogues and diverse insights. Based on these insights and their own evaluation, students proceed to revise the AI draft individually or in groups. Throughout this activity, students are able to develop awareness of AI’s limitations, as well as the necessary skills to critically reflect on the AI-generated content.
The future may well be AI-driven, and it is important to ensure that all students learn how to be effective AI users, with the skills and knowledge to utilize AI to produce the desired outcomes, as well as critically analyze the AI-generated content.
Perplexity AIFbF AI resources hub: A collection of resources on AI including articles, use cases, tools, and more that will help you and your faculty embrace AI technology (such as ChatGPT) in every teaching and learning aspect: from course design, assessment, technology adoption, to policy making.
From factory workers to waitstaff to engineers, AI is quickly impacting jobs. Learning AI can help you understand how technology can improve our lives through products and services. There are also plenty of job opportunities in this field, should you choose to pursue it.
Artificial intelligence (AI) is the process of simulating human intelligence and task performance with machines, such as computer systems. Tasks may include recognizing patterns, making decisions, experiential learning, and natural language processing (NLP). AI is used in many industries driven by technology, such as health care, finance, and transportation.
Learning AI is increasingly important because it is a revolutionary technology that is transforming the way we live, work, and communicate with each other. With organizations across industries worldwide collecting big data, AI helps us make sense of it all.
AI engineers earn a median salary of $136,620 a year, according to the US Bureau of Labor Statistics [1]. Professionals in this field can expect the number of jobs to grow by 23 percent over the next decade.
Artificial intelligence is computer software that mimics how humans think in order to perform tasks such as reasoning, learning, and analyzing information. Machine learning is a subset of AI that uses algorithms trained on data to produce models that can perform those tasks. AI is often performed using machine learning, but it actually refers to the general concept, while machine learning refers to only one method within AI.
Basic statistics: AI skills are much easier to learn when you have a firm grasp of statistics and interpreting data. You’ll want to know concepts such as statistical significance, regression, distribution, and likelihood, all of which play a role in AI applications.
Curiosity and adaptability: AI is complex and rapidly evolving, so there is a constant need to keep up with new techniques and tools. Those looking to pursue a career in AI should have an insatiable thirst for learning and an adaptable mindset for problem-solving.
The depth to which you’ll need to learn these prerequisite skills depends on your career goals. An aspiring AI engineer will definitely need to master these, while a data analyst looking to expand their skill set may start with an introductory class in AI.
Knowing how to code is essential to implementing AI applications because you can develop AI algorithms and models, manipulate data, and use AI programs. Python is one of the more popular languages due to its simplicity and adaptability, R is another favorite, and there are plenty of others, such as Java and C++.
Databases, Algorithms, Data Management, Data Analysis, Computer Programming, Programming Principles, Problem Solving, Computer Networking, SQL, Theoretical Computer Science, Critical Thinking, Xml, Database (DBMS), Json, Web Development, Software Engineering, Data Structures, Computer Programming Tools, Data Visualization, HTML and CSS, Data Analysis Software, Python Programming, Python Syntax And Semantics, Basic Programming Language, Sqlite, Tuple, Data Structure, Web Scraping
A data structure is a specialized format for organizing, storing, retrieving, and manipulating data. Knowing the different types, such as trees, lists, and arrays, is necessary for writing code that can turn into complex AI algorithms and models.
Integer Programming, Approximation Algorithm, Travelling Salesman Problem (TSP), Linear Programming (LP), Algorithms, Theoretical Computer Science, Computer Programming, Analysis of Algorithms, Data Structure Design, Hashtables, Data Structures, Python Programming, Algorithm Design, Graphs Algorithms, Graph Theory, Intractability, Mathematics, Mathematical Theory & Analysis, Problem Solving, Public Key Cryptography, RSA (Cryptosystem), Quantum Algorithms
Data science encompasses a wide variety of tools and algorithms used to find patterns in raw data. Data scientists have a deep understanding of the product or service user, as well as the comprehensive process of extracting insights from tons of data. AI professionals need to know data science so they can deliver the right algorithms.
Data Science, Generative AI, Predictive Modelling, Data Analysis, Model Selection, Data Visualization, Python Programming, Pandas, Numpy, Dashboards and Charts, Matplotlib, dash, Relational Database Management System (RDBMS), Cloud Databases, Jupyter notebooks, SQL, regression, Clustering, SciPy and scikit-learn, classification, Machine Learning, CRISP-DM, Data Mining, Methodology, K-Means Clustering, Github, Jupyter Notebook, Data Science Methodology, Rstudio, Deep Learning, Big Data, Quering Databases, Data Generation, Interviewing Skills, Resume Building, Career Development, Job Preparation
This popular subset of AI is important because it powers many of our products and services today. Machines learn from data to make predictions and improve a product’s performance. AI professionals need to know different algorithms, how they work, and when to apply them.
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