On developing with OpenAI tooling

In creating a workbench to support experimentation with ChatGPT, the example enclosed is the seventeenth thought experiment.   The workbench will probably develop into a case management tool for explicating topics and interests more rigorously than a reader finds here and now.  One way of furthering this development would be to organize thought experiments into use cases reflecting the best practice for asking questions of a certain type.

An area of interest to this author has been determining if ChatGPT can provide benefit in learning development.  Specifically, the learning development latently required to support the adoption of an emerging technology.  There are challenges, either in the form of limits or experience, and threats, either in the form of fear of failure or fear of the unknown, which typically slow the adoption of anything new, in any sphere of interest.

For instance, if we ask ChatGPT: “What are the latent difficulties in developing a learning methodology for an emerging technology?”, what might we get as a response?  The information provided by ChatGPT using the May 3, 2023 Version is provided below:

ChatGPT Response:

Developing a learning methodology for an emerging technology can present several latent difficulties. Here are some potential challenges:

1. Lack of established best practices: With emerging technologies, there may not be well-defined or widely accepted best practices for learning and skill development. This can make it challenging to create a standardized methodology that is effective and efficient.

2. Rapidly evolving landscape: Emerging technologies often evolve at a fast pace, making it difficult to keep up with the latest advancements and incorporate them into the learning methodology. The curriculum and materials may quickly become outdated, requiring constant updates and revisions.

3. Limited availability of learning resources: Compared to established technologies, there may be a scarcity of quality learning resources, such as textbooks, courses, or experienced instructors. It may take time for comprehensive educational materials to be developed and widely accessible.

4. Skill gap among educators: Finding educators who possess deep knowledge and expertise in the emerging technology can be challenging. As a result, training a sufficient number of qualified instructors to teach the subject effectively may take time.

5. Uncertainty and ambiguity: The early stages of an emerging technology often involve uncertainty and ambiguity. There may be multiple competing approaches, lack of standardization, or varying opinions on best practices. This ambiguity can make it challenging to define a clear and structured learning methodology.

6. Limited real-world applications: Depending on the stage of development, emerging technologies may have limited real-world applications or case studies. This can make it difficult to provide practical, hands-on learning experiences for students, which are crucial for skill development.

7. Ethical and societal implications: Some emerging technologies raise ethical and societal concerns that need to be addressed in the learning methodology. For instance, technologies like artificial intelligence, blockchain, or gene editing may require discussions on privacy, security, biases, and broader implications. Incorporating these aspects into the curriculum adds complexity.

8. Scalability and accessibility: Developing a learning methodology that can scale to a larger audience and accommodate learners with diverse backgrounds and skill levels is another challenge. Ensuring accessibility and inclusivity while addressing the unique needs of different learners can be demanding.

9. Limited research and evidence base: Emerging technologies may not have a robust body of research or evidence to support the development of effective learning methodologies. This can hinder the ability to leverage empirical data and proven pedagogical strategies.

Overcoming these difficulties requires a flexible and adaptive approach to learning methodology development. Collaboration between industry experts, academia, and practitioners is vital to address these challenges and create effective frameworks for acquiring skills in emerging technologies.

The ChatGPT process generates its understanding of the question, followed by two sections.  The section after the re-phrasing of the question contains the ChatGPT response, in the context of its understanding of the question.  The third section provides a recapitulation of the answer in abridged form and provides either an admonition to verify the output obtained from ChatGPT and/or pointers to developing the idea further.

The first question we could ask of the response provided is: From our experience and understanding of the challenges confronting those encouraging the adoption of something new, are the identified challenges really challenges?   From personal experience and a history of delivering programs and practices to support the adoption of new technologies, the list of nine characteristics is more inclusive than I ever developed for any one project.

At this point in the thought experiment, it is critical to note that ChatGPT is extremely cognizant of ethical concerns and an overall sense that humanity needs to be considered as an aspect of any development.  Here, those issues are contained in section 7 of the response.  The other eight considerations have all been encountered during one project cycle or another.  Each practitioner could decide how to prioritize the nine elements in the answer, and determine how much emphasis to put on each item, as determined necessary, and when and where in the project cycle.

If one were to add a tenth topic to the solution proposed by ChatGPT, it might involve something about the expected lifecycle of the adoption process.  Lifecycle considerations about productivity improvements or sustainability requirements would help limit the amount of technical debt incurred during the initial learning phases of emerging technology engagement.

Specifying nine out of (a possible) ten characteristics is a very good first experience with a tool.  In the following section, we will further the experiment by surveying ChatGPT for why we should use it to develop training materials.

Section 1.0: What are the top five reasons for using ChatGPT to develop training materials for emerging technologies?

ChatGPT Response:

Using ChatGPT to develop training materials for emerging technologies can offer several advantages. Here are the top five reasons:

1. Language Generation: ChatGPT, as a powerful language model, excels at generating human-like text. It can generate detailed explanations, tutorials, and examples for complex concepts in emerging technologies. This makes it a valuable tool for creating comprehensive training materials that effectively convey information to learners.

2. Versatility: ChatGPT is not restricted to any specific technology or domain. It can be used to develop training materials for a wide range of emerging technologies, such as artificial intelligence, blockchain, quantum computing, robotics, and more. Its flexibility allows it to adapt to various subject areas and cater to different learner needs.

3. Rapid Prototyping: Developing training materials for emerging technologies often requires keeping up with the latest advancements and updates. ChatGPT can assist in rapidly prototyping and iterating on training content. It can quickly generate drafts, outlines, or initial versions of materials, helping trainers and instructional designers streamline their development process.

4. Expert-Level Knowledge: ChatGPT has been trained on a vast amount of text data, including technical documentation, research papers, articles, and more. It has access to a broad knowledge base, which enables it to provide in-depth and accurate information about emerging technologies. Trainers can leverage this expertise to create high-quality training materials that incorporate up-to-date information.

5. Scalability and Availability: ChatGPT is designed to handle a large number of requests simultaneously. This scalability allows for the efficient creation of training materials that can cater to a large user base. Additionally, as an AI model, ChatGPT is available online, making it accessible to trainers and learners worldwide. Its availability ensures that training materials can be developed and delivered to a global audience.

While ChatGPT offers many benefits, it's important to note that human expertise and oversight are still crucial. ChatGPT-generated content should be reviewed, validated, and supplemented by subject matter experts to ensure accuracy and maintain the highest quality standards.

With that information, we can expand our understanding of the capabilities provided by ChatGPT by asking a question about each of the five items provided.  Those questions and answers are provided below, separated by section numbers correlating the order in which ChatGPT provided the items.

Section 1.1: What kind of complex concepts regarding emerging technologies can ChatGPT generate?

ChatGPT Response:

ChatGPT can generate explanations and discussions on a wide range of complex concepts regarding emerging technologies. Here are some examples:

1. Artificial Intelligence (AI):

• Deep learning and neural networks

• Natural language processing (NLP) and language understanding

• Reinforcement learning and autonomous agents

• Computer vision and image recognition

• Generative adversarial networks (GANs)

• Transfer learning and pre-trained models

1. Blockchain Technology:

• Cryptocurrencies and digital assets

• Consensus mechanisms (e.g., proof-of-work, proof-of-stake)

• Smart contracts and decentralized applications (DApps)

• Privacy and security in blockchain

• Tokenization and token economies

• Interoperability and cross-chain solutions

1. Quantum Computing:

• Quantum bits (qubits) and quantum gates

• Quantum superposition and entanglement

• Quantum algorithms (e.g., Shor's algorithm, Grover's algorithm)

• Quantum error correction and fault-tolerant computation

• Quantum supremacy and quantum advantage

• Quantum cryptography and secure communication

1. Robotics and Automation:

• Kinematics and robot motion planning

• Robot perception and sensor fusion

• Human-robot interaction and collaboration

• Autonomous navigation and mapping

• Robot learning and adaptive control

• Industrial automation and robotic process automation (RPA)

1. Internet of Things (IoT):

• Sensor networks and data acquisition

• IoT protocols and communication technologies

• Edge computing and fog computing

• Security and privacy in IoT systems

• IoT analytics and data-driven decision making

• IoT applications in various domains (e.g., smart cities, healthcare, agriculture)

These are just a few examples, and ChatGPT can generate explanations on many more complex concepts related to emerging technologies. The model's ability to generate human-like text allows for detailed discussions, examples, and comparisons, helping learners gain a deeper understanding of these topics.

Section 1.2 How would ChatGPT provide development tools for different learner needs?

ChatGPT Response:

ChatGPT can provide development tools for different learner needs in the following ways:

1. Tailored Explanations: ChatGPT can generate explanations and training materials that are tailored to specific learner needs. By understanding the context and requirements provided by trainers or learners, ChatGPT can adapt its responses to address specific challenges, skill levels, or areas of interest. This personalized approach helps cater to different learning styles and levels of expertise.

2. Adaptive Content Generation: ChatGPT can generate content at varying levels of complexity, from introductory explanations to advanced technical discussions. Depending on the learner's background or proficiency level, ChatGPT can adjust the depth and technicality of the generated content accordingly. This flexibility allows trainers to provide appropriate materials that align with the learners' capabilities.

3. Interactive Examples and Simulations: ChatGPT can generate interactive examples and simulations to enhance the learning experience. For certain emerging technologies, such as programming languages or algorithms, ChatGPT can provide code snippets, walkthroughs, and interactive exercises. Learners can engage with these materials, experiment, and receive feedback, fostering active learning and practical understanding.

4. Visual Aids and Diagrams: ChatGPT can describe complex concepts using visual aids, diagrams, or illustrations. By generating textual descriptions and accompanying visual representations, ChatGPT can help learners grasp abstract or technical ideas more easily. This multimodal approach enhances comprehension and facilitates knowledge retention for learners who benefit from visual learning.

5. Scaffolding and Step-by-Step Guidance: ChatGPT can provide step-by-step guidance and scaffolding to support learners in their understanding and skill development. It can break down complex processes or procedures into manageable chunks, providing clear instructions, checkpoints, or best practices. This approach helps learners gradually build their knowledge and confidence as they progress through the training materials.

6. Contextualized Examples and Use Cases: ChatGPT can generate real-world examples and use cases to demonstrate the practical applications of emerging technologies. By contextualizing the concepts within specific industries or domains, learners can better understand how these technologies are being used and the potential impact they can have. This helps bridge the gap between theory and practice, making the learning experience more relevant and engaging.

By leveraging these capabilities, ChatGPT can provide development tools that cater to different learner needs, including personalized explanations, adaptive content generation, interactive elements, visual aids, step-by-step guidance, and contextualized examples. These tools can support a diverse range of learners, ensuring that the training materials are effective, engaging, and aligned with individual learning preferences and requirements.

Section 1.3: How does ChatGPT provide rapid prototyping and iteration on training content to reduce the step functions in adopting emerging technologies?

ChatGPT Response:

ChatGPT can provide rapid prototyping and iteration on training content to reduce the step functions in adopting emerging technologies through the following ways:

1. Quick Content Generation: ChatGPT can rapidly generate draft content for training materials. This allows trainers and instructional designers to quickly produce initial versions of training resources, such as course outlines, lesson plans, or slide decks. By accelerating the content creation process, ChatGPT enables faster iteration and development cycles.

2. Iterative Feedback and Refinement: Trainers can provide feedback on the generated content, highlighting areas for improvement or requesting additional information. ChatGPT can then incorporate this feedback and generate revised content, enabling an iterative feedback loop. This iterative approach allows for continuous refinement of the training materials, ensuring that they meet the specific needs and preferences of the trainers and learners.

3. Customization and Adaptation: ChatGPT-generated content can be customized and adapted to suit specific training goals or target audiences. Trainers can modify the generated materials, add their own insights, examples, or exercises, and tailor the content to the unique requirements of their learners. This customization capability facilitates rapid adaptation of the training materials to different contexts and learner needs.

4. Stay Up-to-Date with Emerging Technologies: ChatGPT can help trainers stay up-to-date with the latest advancements in emerging technologies. By leveraging its access to a vast amount of textual information, including research papers, articles, and technical documentation, ChatGPT can generate content that incorporates the most recent knowledge and developments. This ensures that the training materials reflect the current state of the technology, reducing the time lag in adopting emerging technologies.

5. Continuous Learning and Improvement: ChatGPT can be fine-tuned and trained further based on the feedback and insights gained during the training material development process. By continuously learning from trainers' expertise and preferences, ChatGPT can improve its performance and generate more accurate and relevant content over time. This iterative learning process helps trainers and ChatGPT collaboratively enhance the quality and effectiveness of the training materials.

By providing rapid prototyping and iterative capabilities, ChatGPT enables trainers and instructional designers to quickly generate, refine, and adapt training content for emerging technologies. This reduces the step functions in the adoption process, allowing for more agile and efficient training material development, ultimately accelerating the learning and adoption of emerging technologies.

Section 1.4:  What is the knowledge base ChatGPT draws up to build training materials for emerging technologies?

ChatGPT Response:

ChatGPT draws upon a vast knowledge base to build training materials for emerging technologies. The model has been trained on a diverse range of text sources, including books, articles, research papers, websites, and other publicly available written content. While the specifics of the training data are proprietary and not publicly disclosed, it encompasses a wide variety of subjects and domains, including technology, science, mathematics, history, and more.

For training materials related to emerging technologies, ChatGPT leverages its understanding of the underlying concepts, principles, and applications from the text data it has been trained on. This knowledge base enables ChatGPT to generate explanations, examples, and discussions about various emerging technologies.

Additionally, ChatGPT can also access the latest information available up until its knowledge cutoff date in September 2021. It can provide insights and explanations based on the advancements and research published up until that point.

It's important to note that while ChatGPT has access to a considerable amount of information, it's always recommended to validate and supplement the generated content with up-to-date and authoritative sources. Emerging technologies are rapidly evolving, and new developments may have occurred since ChatGPT's training data cutoff. Trainers and instructional designers should combine the knowledge provided by ChatGPT with their expertise and current research to ensure the accuracy and currency of the training materials.

Section 1.5: Why is the scalability of ChatGPT an important characteristic for its use in the context of developing material supporting emerging technology programs?

ChatGPT Response:

The scalability of ChatGPT is an important characteristic for its use in developing material supporting emerging technology programs due to the following reasons:

1. Accommodating Large User Base: Emerging technology programs often attract a significant number of learners, including students, professionals, and enthusiasts. ChatGPT's scalability allows it to handle a high volume of requests and generate content simultaneously for a large user base. This ensures that the training materials can be accessed and utilized by a wide audience without experiencing performance or availability issues.

2. Rapid Content Generation: The scalability of ChatGPT enables rapid content generation. As emerging technologies continue to evolve and advance, it's crucial to produce training materials that keep pace with the latest developments. ChatGPT's scalability allows for efficient and timely creation of training content, reducing the time required to develop materials and enabling faster adoption of emerging technologies.

3. Addressing Diverse Learning Needs: Emerging technology programs cater to learners with diverse backgrounds, skill levels, and learning preferences. ChatGPT's scalability ensures that it can provide personalized and adaptable content to meet the varied needs of learners. It can generate content tailored to different proficiency levels, learning styles, and specific requirements, ensuring a more inclusive and effective learning experience.

4. Scaling with Program Expansion: As emerging technology programs grow and expand, the demand for training materials also increases. ChatGPT's scalability allows for seamless scaling to accommodate the growing needs of the program. Whether it's an increase in the number of courses, subjects, or learners, ChatGPT can handle the increased workload, ensuring that the training materials scale alongside program growth.

5. Global Accessibility: Emerging technology programs often have a global reach, with learners from different countries and time zones. ChatGPT's scalability ensures that the training materials can be accessed by learners worldwide, regardless of their geographic location. Its availability and ability to handle concurrent requests facilitate the widespread dissemination and accessibility of the training materials, promoting global knowledge sharing and adoption of emerging technologies.

Overall, the scalability of ChatGPT is crucial in supporting emerging technology programs by accommodating large user bases, enabling rapid content generation, addressing diverse learning needs, scaling with program expansion, and ensuring global accessibility. It allows the training materials to reach a broader audience, support the growth of the program, and keep pace with the dynamic nature of emerging technologies.

In closing, an area we are closely watching is that of insuring proper attribution.  Whether copyright, trademark, or other commercial standard for the re-use of content generated by ChatGPT will come to be recognized, for the purposes of these experiments, the following caveat is provided.

How does a user of ChatGPT properly attribute content to authors if the training data is proprietary and not publicly disclosed?

ChatGPT Response:

As an AI language model, ChatGPT doesn't have direct access to its training data or knowledge of specific documents from the proprietary training set. It has been trained on a mixture of licensed data, data created by human trainers, and publicly available data. Therefore, it doesn't have information about specific authors or texts from the training set.

When it comes to attributing content or information to authors, it is recommended to follow standard academic practices and adhere to copyright laws. If you are using ChatGPT to develop training materials and need to attribute specific content, consider these guidelines:

1. Use publicly available and properly attributed sources: ChatGPT has access to a vast amount of publicly available information. If the generated content aligns with information from publicly accessible sources, you can attribute the information to those sources by providing proper citations or references.

2. Conduct independent research for verification: While ChatGPT strives to provide accurate and reliable information, it's always good practice to independently verify the information by consulting multiple sources. When referencing or attributing content, cross-referencing with reputable and up-to-date sources can help ensure accuracy and provide appropriate attribution.

3. Attribute ChatGPT as the AI tool: When using content generated by ChatGPT, it is essential to acknowledge that the information or text has been generated by an AI language model. You can attribute the content to ChatGPT or OpenAI to indicate the AI assistance used in the creation of the training materials. For example, you can include a statement such as "Content generated with the assistance of ChatGPT, an AI language model developed by OpenAI."

Remember, while ChatGPT can provide valuable insights and information, it's important to exercise critical thinking, independently verify information, and rely on authoritative sources when creating training materials or attributing content.