Libraries and AI (PASCAL): Artificial Intelligence in the Classroom
Using Generative Artificial Intelligence
One challenge of using generative artificial intelligence is the multiplicity of uses that can be employed. This page offers opportunities that AI brings to the practice of teaching, followed by challenges and risks to be considered when prompting and leveraging generative AI in the classroom.
Opportunities for Teaching
Tailored Learning Experiences
Teachers can utilize large language models to customize learning experiences by analyzing students' writing and responses, offering tailored feedback, suggesting relevant materials, ultimately saving time and enabling a focus on more engaging aspects of teaching.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Lesson Plans & Activities
Large language models can aid teachers in crafting inclusive lesson plans and activities by allowing them to input a corpus of documents, generating course syllabi, questions, and prompts tailored to various knowledge levels, fostering critical thinking, and creating personalized practice problems and quizzes to enhance student mastery of the material.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Language Learning
In language classes, teachers can leverage large language models to assist by emphasizing key phrases, generating summaries and translations, explaining grammar and vocabulary, suggesting improvements, and facilitating conversation practice. This approach offers adaptive and personalized support, enhancing the engagement and effectiveness of language learning for students.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Research & Writing
In university and high school classes, teachers can enhance research and writing tasks with the efficient support of large language models, addressing syntactic issues like typos, identifying semantic inconsistencies, and suggesting personalized improvement strategies. These models further aid in topic-specific style enhancement, generating summaries, and creating outlines for complex texts, facilitating a deeper understanding of the content for teachers and researchers.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Assessment & Evaluation
Teachers can leverage large language models to semi-automate grading, highlighting strengths and weaknesses in student work such as essays and research papers, saving time on individualized feedback tasks. These models also assist in plagiarism detection, enhancing the accuracy of assessing student learning development and challenges. Targeted instruction from the models supports student improvement and provides opportunities for further development.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Challenges and Risks
Copyright Issues
When training large language models to generate educational content like course syllabi or scientific papers, there's a risk of plagiarism if the model reproduces sentences or paragraphs from its training data. Mitigating this responsibly involves obtaining permission from original authors, adhering to copyright terms for open-source content, clearly defining terms of use for model-generated content, and raising user awareness of these policies.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Bias and Fairness
Large language models have the potential to amplify societal biases, affecting teaching and learning outcomes, thus requiring diverse and representative training data and regular monitoring to mitigate biases early on. Strategies include employing fairness measures, transparency mechanisms, and providing educators with training to recognize and address biases in model output, alongside continuous updates and human supervision.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Overreliance
Overreliance on large language models by learners and teachers can hinder critical thinking and problem-solving skills. Educators should integrate these models as supportive tools, while promoting diverse learning resources to foster independent inquiry. Caution is necessary when using AI tools for writing, requiring researchers to verify sources and assess their accuracy through examination of bibliographic records.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Data Privacy and Security
Concerns regarding data privacy and security in education arise with the use of large language models, necessitating robust policies compliant with regulations like GDPR, HIPAA, and FERPA, transparency with students and families, advanced data protection measures, regular audits, incident response plans, and education for staff to address ethical concerns and best practices.
This section is adapted from the paper, "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," linked below.Writing Quality Prompts
Prompt engineering is about creating effective queries or instructions that steer a generative AI's output towards the desired outcome. Good prompts lead to more relevant, coherent, and useful results. Mastering prompt design allows educators to refine their questions, getting more precise and context-sensitive answers from AI tools. This skill is crucial for integrating AI into teaching, ensuring it meets educational objectives and delivers reliable content.
Practical Strategies for Prompts
- Clarity and Specificity: Clearly articulate specific queries to avoid ambiguity
- Examples and Context: Provide examples or context to guide the model in producing desired responses
- Experiment with Length: Vary prompt length and detail to optimize results
- Constraints and Guidelines: Set constraints (word count, format) to refine and narrow responses
- Iterate and Refine: Adjust prompts based on initial results for continuous improvement
- Understand Model: Know the model's strengths and limitations for effective prompt tailoring
Example Prompts
Generating Information on Renewable Energy:
- Ambiguous Prompt: "Tell me about renewable energy."
- Specific and Efficient Prompt: "Provide a concise overview of the economic and environmental benefits of solar energy compared to traditional energy sources."
Creative Writing Request:
- Ambiguous Prompt: "Write a story."
- Specific and Efficient Prompt: "Compose a short narrative set in a futuristic world where individuals communicate through holographic messages, exploring the impact on human relationships."
Historical Event Analysis:
- Ambiguous Prompt: "Describe a historical event."
-
Specific and Efficient Prompt: "Analyze the geopolitical factors leading to the outbreak of World War II, focusing on the role of key nations and the series of events that unfolded in the years preceding the conflict."
FREE Generative AI tools
- ChatGPT (OpenAI)
ChatGPT is an AI language model developed by OpenAI, designed to understand and generate human-like text based on the input it receives, offering conversational capabilities and assistance across various tasks and domains. - Copilot (Microsoft)
(formerly Bing Chat) Microsoft Copilot is a chatbot based on a large language model and able to cite sources, create poems, and write both lyrics and music for songs generated by its Suno AI plugin. It is Microsoft’s primary replacement for the discontinued Cortana. (Wikipedia) - Gemini (Google)
Gemini is a family of multimodal large language models developed by Google DeepMind, serving as the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, and Gemini Nano, it was announced on December 6, 2023, positioned as a competitor to OpenAI's GPT-4. (Wikipedia) - DALL-E 2 (OpenAI)
DALL-E 2 is an AI model developed by OpenAI that generates images from textual descriptions, allowing users to input text prompts and receive corresponding synthetic images as outputs, demonstrating advanced capabilities in creative visual synthesis. - fireflies.ai
Fireflies.ai is a voice conversation tool that records, transcribes, and analyzes meetings, offering AI-powered search, collaboration features, and insights through Conversation Intelligence metrics.
References and Further Resources
- ChatGPT for Good? On Opportunities and Challenges of Large Language Models for EducationPresents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives.
- Practical Strategies for ChatGPT in educationThis post covers six areas where teachers could use ChatGPT with examples of the kinds of prompt you can use to get the best results.
- ChatGPT: Implications for academic librariesThe abstract introduces ChatGPT, an LLM by OpenAI that gained viral popularity upon its late 2022 release. Trained on a vast corpus of text data, including multiple languages and domains, ChatGPT utilizes deep learning to generate diverse content in response to user queries, ranging from essays to medical diagnoses, by predicting subsequent words in a sequence.
- ChatGPT in Education: Partner or Pariah?ChatGPT has taken the world by storm, with educators reeling from its implication for curricula and assessment. This article examines how ChatGPT resembles earlier technologies and predicts how we can expect it to impact education going forward.