AI literacy has become essential for researchers at all stages of their careers, as it provides the foundational knowledge and practical skills necessary to incorporate AI tools into the research process effectively. According to Ng et al. (2021), AI literacy is defined as the knowledge, skills, and attitudes required to understand and use AI tools in various contexts, including research, data analysis, and decision-making. This helps researchers automate tasks, process large datasets, and gain insights from data, thus enhancing their productivity and improving the quality of their research outcomes.
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How we phrase our questions to an AI tool significantly impacts the responses we receive. Therefore, it is beneficial to develop skills and techniques that can improve the effectiveness of our interactions with AI. |
AI Prompt Anatomy
A well-constructed prompt ensures that the AI understands the task, processes the input accurately, and provides useful and relevant output. To achieve this, it is important to follow a few key steps.
- Provide context: Clearly outline the background of the input data, such as a dataset.
- Include instructions: Specify what needs to be done.
- Define expected output: Clearly state what you are looking for and the parameters you want to set.
- Once you receive the results, be sure to modify them if necessary and keep track of any changes made.
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Context |
This step provides background information to frame the query, such as the research question, objectives, or purpose of the analysis. |
| Instructions | The core of the prompt - what you are asking the AI to do. This section should be clear and specific to ensure the AI understands the task. |
| Input Data | The dataset or relevant information provided to the AI to process. This could include raw data, images, text, or other forms of input. |
| Expected Output | What you want the AI to return - whether it is a summary, classification, analysis, or some other result. |
| Parameters | Any additional conditions or specifications for the AI’s response, such as formatting, length, or tone. |
| Revisions / Versioning | Track modifications made to the prompt. Version control ensures that previous iterations are noted for consistency and improvement. |
4 Key Components of AI Literacy (Anders, 2023):
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Awareness |
Understanding AI's Role: Recognise how AI affects everyday life and society. Ethical Issues: Understand the ethical concerns and possible biases in AI systems. AI in Media: See how media portrays AI and know the difference between fiction and reality. |
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Capability |
Practical Skills: Learn how to use AI tools and technologies effectively. Problem-Solving: Use AI to solve real-world problems and make tasks more efficient. Hands-On Practice: Get involved in projects and activities that give you practical experience with AI applications. |
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Knowledge |
Fundamental Concepts: Grasping the basic principles and theories of AI, including machine learning. Historical Context: Exploring the history and evolution of AI technologies. Current Trends: Keeping up to date with the latest advancements and trends in the AI field. |
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Critical Thinking |
Analytical Skills: Evaluate AI systems to understand their strengths and weaknesses. Decision-Making: Make informed choices about how to use and implement AI in different situations. Future Implications: Think about the long-term effects of AI on society and possible future changes. |