50 Things Every Teacher Needs To Un-learn About ChatGPT (& GPTs)
I came across an interesting video on MoodleMoot Global 2023 platform. The topic of discussion was “AI. Is this the end of education as we know it?” Here is the video for anyone who wants to see it as well.
This video prompted me to think about what are some 50 things that teachers might have got wrong on AI.
Artificial Intelligence has become a transformative force in education, and within this landscape, models like GPT (Generative Pre-trained Transformer) have garnered significant attention. Teachers play a pivotal role in shaping how students understand and utilize this technology. However, amidst the buzz and excitement, misconceptions about GPT have proliferated.
Let’s debunk 50 common assumptions that educators might have about GPT.
- GPT Isn’t All-Knowing: Despite its vast knowledge, GPT models are not omniscient; they generate responses based on patterns in data.
- GPT Isn’t a Human: It can mimic human-like responses, but it lacks consciousness, emotions, or true understanding.
- Not All GPT Versions Are Equal: Different iterations (like GPT-2, GPT-3, etc.) vary in size, capabilities, and training data.
- GPT Isn’t Free from Bias: Its responses may reflect biases present in the training data.
- It’s Not Always Accurate: GPT generates responses based on statistical likelihood, not absolute truth.
- GPT Needs Monitoring: It can generate inappropriate or misleading content.
- GPT Can’t Replace Critical Thinking: Students should critically evaluate GPT-generated content.
- Not All GPT Uses Are Ethical: Misuse of AI can lead to ethical issues, including plagiarism.
- GPT Doesn’t Understand Context Perfectly: It lacks contextual understanding and might produce irrelevant responses.
- It Doesn’t Have Common Sense: GPT lacks everyday knowledge and might give absurd answers.
- GPT Can’t Learn From Interactions: It can’t learn or improve in real-time based on user interactions.
- GPT Isn’t Always Reliable for Sensitive Topics: It might mishandle sensitive or personal information.
- GPT Can’t Replace Human Teachers: It’s a tool to assist teachers, not a substitute for their expertise.
- It Isn’t Always Legal to Use GPT: Depending on the use case and data, legal implications may arise.
- GPT Isn’t Always Safe: It can be manipulated to generate harmful content.
- Not All GPT Outputs Are Original: It may paraphrase or reproduce content from its training data.
- GPT Doesn’t Create Real Conversations: It generates responses based on patterns, not genuine dialogue.
- It Isn’t Always Suitable for Younger Audiences: Inappropriate content might be generated.
- GPT Isn’t Always Transparent: Understanding its decision-making process might be challenging.
- GPT Doesn’t Understand Emotional Nuances: It can’t comprehend emotions expressed in text accurately.
- It Isn’t Always Up-to-Date: GPT might lack current information depending on its training data.
- GPT Can’t Solve All Problems: It has limitations in problem-solving and complex tasks.
- GPT Doesn’t Understand Visuals or Multimedia: Its training is based solely on text.
- It Can’t Provide Personalized Feedback: Generic responses might not suit individual needs.
- GPT Can’t Learn in Real-Time: It’s not adaptive based on immediate feedback.
- Not All GPT Uses Respect Privacy: It might compromise privacy when handling sensitive data.
- GPT Isn’t Always User-Friendly: Some implementations might be complex or challenging to use.
- It Doesn’t Recognize Irony or Sarcasm: Literal interpretations might lead to misunderstandings.
- GPT Isn’t Perfectly Secure: Vulnerabilities might exist, leading to potential exploitation.
- It Can’t Explain Its Reasoning: GPT lacks the ability to explain how it arrived at a specific response.
- GPT Doesn’t Always Respect Copyright: Plagiarism issues might arise.
- GPT Isn’t Always Accessible: Some implementations might exclude certain users due to technical or language barriers.
- It Can’t Learn From Physical Experiences: GPT’s learning is confined to text data.
- GPT Doesn’t Have Intuition or Instincts: It lacks intuition-driven decision-making.
- Not All GPT Implementations Have Clear Ownership: Ownership and responsibility might be ambiguous.
- GPT Can’t Provide Original Research: It’s not designed to conduct new studies or experiments.
- It Doesn’t Understand Linguistic Variations Equally: Bias might exist in different language models.
- GPT Can’t Account for Different Cultural Contexts: Responses might not consider diverse cultural nuances.
- It Doesn’t Have Real-World Experiences: GPT lacks lived experiences to draw upon.
- GPT Can’t Generate Infinite Content: It has limitations on generating extensive, coherent content.
- Not All GPT Uses Align With Educational Goals: Some applications might not serve educational purposes effectively.
- GPT Isn’t Always Trustworthy: It might generate misleading or false information.
- It Can’t Adapt to Individual Learning Styles: GPT lacks the ability to tailor responses to different learning approaches.
- GPT Isn’t a Replacement for Peer Interaction: Real interactions with peers are crucial for holistic learning.
- GPT Can’t Recognize User Identity: It treats all queries equally without recognizing individual users.
- It Doesn’t Always Prioritize Relevance: Responses might lack relevance to the query.
- GPT Can’t Predict Future Events Accurately: It lacks predictive abilities beyond statistical patterns.
- It Can’t Interpret Complex Instructions Well: Ambiguous or intricate instructions might confuse GPT.
- GPT Isn’t Always Monitored for Misuse: Lack of oversight can lead to misuse.
- It Doesn’t Learn Ethics Inherently: Ethical considerations must be imparted by users; GPT lacks inherent ethical understanding.
Understanding these misconceptions is pivotal for educators and students alike. While GPT holds immense potential, it’s crucial to navigate its limitations and functionalities responsibly.
Understanding GPT to Understand its true Utility
At its core, GPT, or Generative Pre-trained Transformer, operates as a text-based predictive model. It’s a type of artificial intelligence that generates human-like text responses based on patterns and structures it has learned from a vast amount of text data. It achieves this through a process called “pre-training,” where it learns the statistical relationships between words and phrases in the text it has been exposed to.
The model’s ability to produce coherent and contextually relevant text is not driven by true understanding or consciousness, but by recognizing statistical patterns. It doesn’t possess consciousness, emotions, or a genuine understanding of the information it processes. This limitation stems from the fact that GPT interprets and generates responses based on patterns, lacking inherent comprehension or emotional understanding.
Several factors contribute to the limitations of GPT:
- Training Data and Variants: Different versions of GPT (such as GPT-2, GPT-3, etc.) vary in size, capabilities, and the diversity of data they’ve been trained on. This variance affects their performance and the accuracy of generated responses.
- Biases in Training Data: GPT’s responses may reflect biases present in the data it was trained on. Biased or skewed data might lead to biased or inaccurate outputs, impacting the reliability of information.
- Statistical Likelihood vs. Absolute Truth: GPT generates responses based on statistical likelihood rather than absolute truth. This means that while it might offer seemingly accurate information, it’s not infallible and may not always produce completely accurate or reliable content.
- Lack of Contextual Understanding: GPT might struggle with understanding context perfectly and may produce irrelevant or inadequate responses when faced with intricate or ambiguous instructions.
- Inability to Predict or Learn Real-Time Interactions: GPT can’t accurately predict future events or improve in real-time based on user interactions. It lacks the ability to learn from user feedback or interactions to adapt its responses.
- Ethical Considerations and Misuse: GPT’s usage isn’t inherently ethical; it can generate inappropriate, misleading, or plagiarized content if not monitored or used responsibly.
These technical constraints and limitations can hinder GPT’s integration into classrooms:
- They might lead to the generation of inaccurate or misleading content, posing challenges in teaching accurate information.
- Overreliance on GPT without critical evaluation could hamper students’ development of critical thinking skills.
To address these challenges, teachers should educate students about the limitations of GPT, emphasize the importance of critical evaluation, and implement monitoring mechanisms to ensure responsible and ethical use of AI-generated content. Additionally, providing guidance and supplementary teaching materials alongside GPT-generated content can help foster a more comprehensive and accurate learning experience.
The multitude of limitations and challenges associated with GPT can indeed pose hurdles in its seamless integration into classrooms, impacting the learning experience. These limitations create barriers that educators need to address to ensure responsible and effective use of AI technology:
- Reliance on Inaccurate Information: GPT’s inability to ensure absolute accuracy might lead to the dissemination of misleading or false information in educational settings. This can hinder the learning process and instill misconceptions among students.
- Undermining Critical Thinking: Over-reliance on GPT-generated content without critical evaluation might discourage students from developing their critical thinking skills. Students might accept information at face value without questioning or verifying its accuracy.
- Ethical Concerns and Plagiarism: GPT’s potential for generating content without proper attribution or originality can raise ethical concerns, especially regarding plagiarism. Students might inadvertently use content without proper citation, leading to ethical issues in academia.
- Misinterpretation of Context: GPT’s limitations in understanding contextual nuances might result in irrelevant or inadequate responses. This can hinder effective communication and comprehension of complex topics within educational contexts.
Here are some strategies for Educators and Teachers to address these Shortcomings of GPTs in Classroom
- Promoting Critical Evaluation: Emphasize the importance of critically evaluating GPT-generated content. Educate students about the limitations of AI and the significance of verifying information from multiple reliable sources.
- Teaching Ethical Use: Educate students about ethical considerations in using AI. Highlight the importance of proper citation, attribution, and responsible use of AI-generated content.
- Supplementing GPT Outputs: Encourage the use of GPT-generated content as a supplementary resource rather than a primary source of information. Supplement it with teaching materials and guide students in cross-referencing information.
- Monitoring and Oversight: Implement monitoring mechanisms to review and ensure the appropriateness and accuracy of GPT-generated content used in classrooms. Teachers should maintain oversight and guide students in responsible use.
- Fostering Critical Thinking: Design activities that promote critical thinking, problem-solving, and independent research alongside the use of AI. Encourage students to engage actively in evaluating, questioning, and synthesizing information.
By proactively addressing these barriers through education, oversight, promoting critical thinking, and ethical use of AI, educators can navigate the challenges posed by GPT’s limitations and ensure a more responsible and effective integration of AI technology in classrooms.
GPT in Classrooms : Revisiting the SAMR Model for AI Integration
The SAMR model, focusing on Substitution, Augmentation, Modification, and Redefinition, outlines how educators can integrate technology into teaching practices. (Click here to Read More on SAMR Model for Educators)
Let’s break down how GPTs (whether it it ChatGPT or any other LLM and AI technology) can infact be practically integrated in classroom teaching using each block of the SAMR model :
- Substitution: At this level, technology acts as a direct substitute with no functional change. In the context of AI integration, teachers use technology (like speech recognition algorithms and AI chatbots) to replace traditional methods of recording and analyzing classroom interactions. For instance, instead of manually timing and analyzing classroom talk time, teachers use AI-powered tools to transcribe, analyze, and provide data insights on student-teacher interactions.
- Augmentation: Technology not only substitutes but also adds functionalities that enhance the task. AI tools based on data analytics platform, go beyond mere substitution. They provide deeper insights into teaching practices by analyzing various aspects of classroom interactions, like question types, student engagement, and use of academic language. This augmented functionality supports teachers in identifying strengths and weaknesses, aiding in self-reflection and professional growth.
- Modification: Here, technology enables significant task redesign. The AI tool not only analyzes data but also presents findings conversationally, similar to ChatGPT. It doesn’t just offer static reports; it engages teachers in reflective conversations, posing thought-provoking questions and providing actionable insights. This modification redefines how teachers engage with data analysis, fostering deeper reflection and understanding.
- Redefinition: At the highest level, technology allows for the creation of new tasks or fundamentally changes the learning experience. AI-powered coaching tools, introduce a novel approach to coaching. Teachers can engage in reflective practices independently, leveraging AI assistance to guide their reflection on teaching practices through video annotations and reflective prompts. This redefines the coaching process, empowering teachers to drive their professional growth autonomously.
By aligning AI tools and practices with the SAMR model, educators leverage technology to not only enhance but also transform teaching practices.
Digital Bloom’s Taxonomy for AI integration in Classrooms
The Digital Bloom’s Taxonomy, or sometimes referred to as Bloom’s Digital Taxonomy, is an adaptation of the traditional Bloom’s Taxonomy by Andrew Churches, developed to align with the digital age and technological advancements in learning. It aims to integrate verbs and activities specific to digital learning and creating within the framework of cognitive processes.
Here’s an overview of the Digital Bloom’s Revised Taxonomy with examples of verbs and activities for each level:
- Activities include bookmarking web pages, conducting online searches, linking resources, and using search engines to retrieve information.
- Engaging in activities such as annotating digital content, conducting Boolean searches for refined information, maintaining digital journals, and participating in concise communication through platforms like Twitter.
- Involves utilizing digital tools to create charts, executing tasks through software or applications, displaying data or information, making presentations, and uploading content to online platforms.
- Activities encompass attributing sources in digital content, deconstructing information or media, creating visual representations like mind maps or infographics, and synthesizing content through mashups or remixes.
- Includes activities like commenting or providing critiques on digital content, moderating online discussions, networking with peers or professionals, and posting or sharing opinions or analyses.
- Engaging in activities such as blogging, creating digital films or videos, integrating multimedia content, producing podcasts, programming software or applications, and publishing digital content online.
This taxonomy aims to incorporate digital skills and tasks relevant to the modern digital landscape into the traditional hierarchy of cognitive processes, providing educators with a framework to design learning experiences that harness the potential of technology for enhanced learning and creation in the digital era.
Teachers aiming to integrate SAMR for AI in classroom also need to acquaint themselves with the the digital Bloom’s Revised Taxonomy. This will indeed benefit from understanding the synergy between these frameworks. Here’s what they need to know:
- Enhancement vs. Transformation: SAMR highlights technology’s progression from substitution to redefinition. When integrating AI, teachers should understand that not every task needs redefinition; incremental shifts in technology use can still yield significant benefits.
- Flexibility and Simultaneous Movements: Teachers can move between SAMR levels depending on the task’s nature, allowing for flexibility in integrating AI. It’s acceptable to enhance some tasks while transforming others.
- Coupling with Other Frameworks: SAMR’s adaptability makes it complementary to other frameworks like Bloom’s Taxonomy. Teachers can leverage SAMR to incorporate AI tools at different levels while aligning tasks with learning objectives.
Integrating frameworks for Effective Implementation of GPTs in Classroom
For effective integration Bloom’s Taxonomy can be integrated with SAMR to ensure that technology use aligns with higher-order thinking skills. AI tools can aid in tasks at various Bloom’s levels, from basic recall to advanced creation.
- Training and Professional Development: Teachers should receive training on using AI tools and strategies for integrating them at different SAMR levels while aligning with Bloom’s Taxonomy.
- AI for Diverse Learning Styles: AI can support different levels of Bloom’s Taxonomy, catering to diverse learning styles and abilities. For example, AI-powered tools can offer personalized learning experiences, supporting students in various cognitive domains.
- Task Design Alignment: Tasks designed with technology integration should align with learning objectives and Bloom’s cognitive levels. Teachers should ensure that AI integration enhances critical thinking, creativity, and problem-solving.
- Alignment with Learning Objectives: Digital Bloom’s Revised Taxonomy focuses on cognitive processes, from remembering to creating. When using AI, teachers should align technology integration with these cognitive levels. For instance, using AI for research (Remembering) or generating innovative solutions (Creating).
Integrating AI like GPT effectively into education requires a nuanced understanding and proactive measures to maximize its benefits while mitigating its drawbacks. These AI-powered tools facilitate deeper reflection, data-driven insights, and personalized professional development, ultimately leading to improved teaching strategies and student learning outcomes. Understanding the alignment between SAMR, AI integration, and Bloom’s Taxonomy empowers teachers to use technology effectively, ensuring that AI tools enhance learning experiences across various cognitive domains and levels of complexity.