Category: AI & Education

We have passed mid-summer and educators are beginning to think about their fall classes if they have been doing so for some time. AI is one of those innovations that may be dominating the thinking of some. Do I ignore AI or address it directly? What am I going to do about cheating? Are there specific tools or tactics I should be teaching (or avoiding)? 

If you think I have the answers, it is time to move on to another writer. I have been using AI a lot myself and I have been doing my best to locate and review articles on AI in classrooms. I have made a few personal decisions and I have written about them in previous posts. I have decided for now my own work will make use of AI tools that allow me to focus AI on content I designate. So, I am using tools that require that I submit a pdf to be the target of my prompts or that work within a note-taking system I use and can be focused on my own notes and highlights. This works for me. I don’t get assigned tasks from someone else that I must submit to be evaluated. My productivity goals and thoughts about why I benefit from writing guide my choices. Educators may have similar goals for their own work, but also are looking to develop the skills of their students and it is this responsibility that makes things much more complex.

Here is a resource that may be useful. It is not heavy on specific recipes for the use of AI in your classroom, but may be useful if you want a good explanation of just what generative large language model AI services are and an analysis of concerns and opportunities for the application of such services by those who teach writing. If I were to quibble with the authors, it would involve failing to pay enough attention to what I would describe as writing to learn and learning to write. I apologize for that turn of a phrase, but it is one in my bag I like to pull out. My work has often involved researching and proposing the classroom use of generative learning activities. I like to describe generative activities as external tasks intended to encourage productive internal (cognitive) behaviors. So, tasks a learner performs that require productive thinking activities a learner may or may not exercise on their own. Writing to learn is an example and it is based on the expectation that writing requires organization and communication of information you have or can acquire as a benefit to understanding, retention, or application in a way that might not occur if you just tried to think about this information. By definition, it is labor (thinking) intensive and may not be the most efficient way to accomplish understanding, retention, and application. It works because the tasks involved in writing to communicate require work focused on the manipulation of the to-be-learned content. Here is my thought related to AI – tools that make the processes of organization and description less labor intensive may eliminate the cognitive work that may be productive in the process of learning. Producing a better written product or writing more efficiently are different goals. Offloading subcomponents of writing may be helpful in writing and the purposeful control of this offloading may help develop the skills of writing. Hopefully, this differentiation makes some sense and is meaningful.

So, what do those who study the development of writing skills have to say about AI. The source I am recommending here was developed by experts from the task force associated with the Modern Languages Association charged with developing a working paper on AI and writing instruction. As I have explained already this product steers clear of specific classroom recipes, but identifies legitimate concerns, likely benefits, and proposed actions to benefit writing educators. I will summarize these areas, but encourage writing people review this document. It is not unnecessarily lengthy and to my eye represents a balanced analysis.

The advantages of AI for writing are as follows:

• Personalized feedback and support for language learners: AI can provide personalized feedback to language learners, helping them to improve their writing skills. This can be especially helpful for students who are struggling with a particular aspect of writing, such as grammar or punctuation.
• Ability to analyze large amounts of text for literary scholars: AI can analyze large amounts of text, helping literary scholars to identify patterns and trends. This can be helpful for research and for understanding the development of literature over time.
• Ability to assist students with tasks such as generating ideas, organizing their thoughts, and identifying errors in their writing: AI can assist students with tasks such as generating ideas, organizing their thoughts, and identifying errors in their writing. This can help students to improve their writing skills and to produce higher-quality work.
• Potential to democratize writing and make it more accessible to a wider range of learners: AI has the potential to democratize writing and make it more accessible to a wider range of learners. This is because AI can provide personalized feedback and support, and can assist students with tasks such as generating ideas and organizing their thoughts.

The disadvantages of AI for writing are as follows:

• Risk that students may rely too heavily on AI-generated outputs and miss out on important writing, reading, and thinking practice: There is a risk that students may rely too heavily on AI-generated outputs and miss out on important writing, reading, and thinking practice. This is because AI can generate text that is grammatically correct and that sounds good, but that may not be accurate or well-informed.
• Risk that students may submit AI-generated work as their own, which could lead to issues with academic integrity: There is also a risk that students may submit AI-generated work as their own, which could lead to issues with academic integrity. This is because AI can generate text that is indistinguishable from human-written text.
• Potential for bias in AI systems: Finally, there is the potential for bias in AI systems. This is because AI systems are trained on data that is collected from the real world, and this data may be biased. This means that AI systems may generate text that is biased, which could have negative consequences for students and for society as a whole.

 The MLA also provided the following policy recommendations.

• Writing instructors should recognize that there may be equity issues in the use of AI tools and work to provide equal access to all students.
• Writing instructors should engage in ongoing professional development to stay up-to-date on the latest developments in AI and writing instruction.
• Writing instructors should collaborate with colleagues, students, and other stakeholders to ensure that the use of AI in writing instruction is effective and ethical.
• Writing instructors must emphasize the ethical responsibility that comes with the use of AI tools and spend time with students to consider the misrepresentation of authorship.

Source:

MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement of Principles, and Recommendations. Available online: https://hcommons.org/app/uploads/sites/1003160/2023/07/MLA-CCCC-Joint-Task-Force-on-Writing-and-AI-Working-Paper-1.pdf

89 total views

Humata AI is another of those AI tools for exploring designated content. It is being promoted as a tool for researchers, but its use is not limited to any specific category of content explorers. An easy comparison would be ChatPDF as the service allows a user to upload and then interact with a pdf. However, the “pro” version also allows a user to interact with a collection of documents (see my description of other services with this capability). 

You can presently explore the capabilities of this service at no cost for individual documents. The Pro version is $15 a month for 250 pages and an additional penny a page after that page allocation is exhausted. 

Humata automatically generates a short summary of the document uploaded and proposes some questions. It is not clear to me which large language tool is being used to power this service. The product description proposes that a user can ask for descriptions (summaries), ask questions, and generate responses and write material based on the content that is uploaded. If you have used other AI tools, you can use this tool in a similar way and just see what it will do in response to requests.

The one feature I found uniquely useful in comparison to most of me experiences with other tools is that it assumes you may want to connect the content generated with the source material. It will both highlight and link to this material in an adjacent window (see image). 

Here is another description of this product.

65 total views

With the recent attention paid to AI because of large language tools such as ChatGPT, my title’s promise to describe change my surprise most educators. Tools for AI tutoring have been deployed and evaluated for some time. I provide a citation in support of this claim at the conclusion of this post. This paper is worth exploring because it offers insights into what has been meant by AI when applied to tutoring and even some insights into human tutoring. For example, I have often quoted the work of Bloom who argued that human tutors probably represented the ideal in teaching and suggested that dedicated intensive tutoring provided a two-standard deviation advantage to those tutored and this advantage represented the best educational interventions could accomplish. I have read the papers in which Bloom made this claim. Kulik and Fletcher offer a different interpretation explaining that Bloom’s data actually involved a combination of tutoring and a mastery approach and the mastery approach may have accounted for at least half of the benefit in this research. That aside, tutoring still offers learners a significant advantage.

Before AI was based on large language models, the AI involved in tutoring was based in a technology-supported system based on a model of what was to be learned, a model of the individual learner, a model of effective instructional strategies, and an interface allowing communication with the system. I had AI generate a description of what the researchers explained these three models involved.

1. Learner Model: This model represents the student’s knowledge, skills, and learning preferences. It helps the ITS to adapt its teaching strategies to the individual needs of the student.
2. Teacher Model: This model represents the teaching strategies and pedagogical knowledge used by the ITS to guide the student’s learning process. It helps the ITS to provide appropriate feedback, hints, and explanations.
3. Content Model: This model represents the subject matter being taught by the ITS. It includes the concepts, relationships, and problem-solving procedures relevant to the domain.

The Kulik review found generally positive benefits for the AI studies, but indicated impact was smaller when the dependent measure was a standardized test rather than local tests, the sample size was small, learners were from the lower grades, the subject was math, MC tests were used as the dependent variable, and the tool studied was Cognitive tutor. For those interested in this type of approach, the review identifies a number of the systems available for use.

My interest in the potential application of the AI tools now available takes a somewhat different approach and suggests that educators and researchers begin with an analysis of the techniques used in successful studying and tutoring and attempt to translate these techniques into tasks that students or educators can apply using AI. I purposefully focus on the research on studying as a general way to think about the cognitive activities of learners following initial experiences which could involve lectures, readings, or any observation of what happens in the world. Simply put, learning requires the processing of external experiences for understanding, retention, and application whether entirely internally and unaided or encouraged by additional external activities (e.g., taking notes, answering questions, discussions with a partner). The natural language capabilities of large language AI allow approximation of these external activities. I have attempted to demonstrate what some of the activities might look like in an earlier post. 

My prediction is that companies serving the education market will quickly combine the type of AI approaches I have described here (the multi-model approach with the more flexible capabilities of large language models) because of the resources required to do so. You may already see the direction in which this is going be taking note of the efforts of the Kahn Academy (Kahnmigo). 

I do think there are immediate opportunities to take advantage of the tools now available. One distinction that I think educators should consider involves whether activities are applied to the knowledge base used to train the models or applied to designated content. For example, you have ChatGPT ask you multiple choice questions about mitosis or you can ask some AI tools to ask you questions about a pdf or designated content about mitosis. My prediction about commercial ventures will likely be more like this second option as a textbook company would like to focus an AI tutoring approach focused on individual textbooks. There is nothing inherently wrong with either system. I do believe there are more errors when using the more general knowledge base. I also see advantages in controlling the scope of experiences to a designated body of content as a way to control the depth of the experiences. What I mean the discussion of mitosis in a sophomore biology class will be different than in a graduate class because the knowledge base would be different.

I have explored the use of ChatPDF (not ChatGPT) in engaging with the content of a pdf in a previous post. I have been exploring Mem.AI as a way to engage in similar interactions with notes I have taken within this service (you do need the $8 a month version). What I like about this system is that it allows a user to focus the AI prompts on specific material. For example, I could use the service to take class notes on a given day, tag these individual notes or a large page of notes with the date, and then write a prompt to engage with the content identified by this tag. I could also tag notes with a class identifier (PSY101) and then write prompts focused on this tag to review for exams covering more material. I have included several different types of interaction in the material that follows. This material includes my prompts and the AI responses. 

***********************

Me: Using only my knowledge base with tag generative, write a summary of the activities that are classified as generative. Include a list of notes providing this information.

Generative learning activities involve actively engaging with the material to create new knowledge and connections. Some key strategies that promote generative learning include summarization, teaching, self-testing, self-explaining, and notetaking with revision and elaboration. These strategies require learners to select and organize relevant information, elaborate on the material based on personal knowledge, and integrate new information with existing knowledge.

Here are the notes from your knowledge base with the tag “generative”:

1. Brod’s generative
2. Summarize my notes on generative learning
3. Cognitive Activities

These notes provide valuable insights into generative learning strategies and their effectiveness across different age groups, as well as the cognitive processes involved in these activities.

Me: Using only my knowledge base with tag june9, write a summary of the models the author described

Based on your knowledge base with the “june9” tag, here’s a summary of the models described by the author:

In the review by AlShaikh and Hewahi (2021), the authors discuss the development of Intelligent Tutoring Systems (ITS) and their use of multiple models. These models include:

1. Learner Model: This model represents the student’s knowledge, skills, and learning preferences. It helps the ITS to adapt its teaching strategies to the individual needs of the student.
2. Teacher Model: This model represents the teaching strategies and pedagogical knowledge used by the ITS to guide the student’s learning process. It helps the ITS to provide appropriate feedback, hints, and explanations.
3. Content Model: This model represents the subject matter being taught by the ITS. It includes the concepts, relationships, and problem-solving procedures relevant to the domain.

These models work together to create a personalized learning experience for the student. However, the authors note that recent large language models and conversational approaches offer new possibilities for influencing learning without built-in strategies, which could be an important difference in the application of newer general tools.

Me: Using only my knowledge base with tag june9, write a multiple choice questions for me to answer.

Based on your knowledge base with the “june9” tag, here’s a multiple-choice question for you to answer:

In the context of Intelligent Tutoring Systems (ITS) described by AlShaikh and Hewahi (2021), which of the following models is responsible for representing the student’s knowledge, skills, and learning preferences?

A. Learner Model B. Teacher Model C. Content Model D. Language Model

Me: A

That’s correct! The Learner Model in Intelligent Tutoring Systems is responsible for representing the student’s knowledge, skills, and learning preferences. Great job! If you have any more questions or need further clarification, feel free to ask.

References: 

Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of educational research, 86(1), 42-78.

62 total views

I am intrigued by large language AI applications, but I admit I am confused. It is not so much trying to understand at a deep level how these services do what they do. I gave up on investigating the “how” question long ago. I am confused by pricing. What am I likely to have to pay say six months from now when we get past the free experimental stage? I understand that ChatGPT Plus is $20 a month. This seems like a lot. What makes it seem like a lot is the price I pay for the various services I pay for as an API allowing access through some other service.

It has taken me some time even to figure out which AI activity I use through some service other than ChatGPT or Bard, I actually am paying for. I know I have an OpenAI API account (so Bard is not included) and I know I have had to generate and attach licenses to other services. I should have labeled the licenses from the start. That would have made identifying where my charges come from easier. However, whichever services generate a cost, I can’t seem to run up much of a bill.

I am not a heavy user of AI services, but I do spend time each day exploring. So far, my bill for the month is a nickel. If the rate for ChatGPT Plus is the same as for the API use, I can’t imagine what it would take to run up a $20 bill.

For the curious, the following is the OpenAI Usage page allowing users to set a limit. I originally thought this limit was just a sum I would allocate and then utilize, but it turns out it is the limit I set for myself for a month. Obviously, this is far too high, but you pay only for what you use (sounds like a television ad). I have no idea how usage fees will be set in the future, but for now you get a lot of activity for little money.

I have tried to find clear descriptions of fees online, but I cannot find a useful summary of pricing and options. I did finally decided just to ask ChatGPT.

What is the difference between OpenAI API prices and ChatGPT Plus?

OpenAI API prices and ChatGPT Plus are two different products, with different features and pricing models.

OpenAI API is an artificial intelligence platform that provides developers with access to a wide range of state-of-the-art models, including natural language processing, machine learning, and computer vision. OpenAI API pricing is based on usage, with different rates for each API endpoint. Developers are charged based on the number of requests made to the API, with pricing starting at $0.001 per request.

ChatGPT Plus, on the other hand, is a specific natural language processing tool that uses the GPT-3 language model. It is designed for chatbot development, and includes features such as sentiment analysis, entity recognition, and conversational response generation. ChatGPT Plus is priced per month, with different pricing tiers based on the number of API calls and the level of support provided.

In short, OpenAI API provides access to a wide range of AI models with pricing based on usage, while ChatGPT Plus is a specific chatbot development tool based on the GPT-3 model with monthly pricing.

41 total views

I was listening to a recent podcast from This Week in Tech (TWIT) and the program began as so many podcasts I listen to recently with a lengthy discussion of AI. Part of the discussion concerned the role AI tools will likely play in the election of 2024. Besides the concern that AI would likely be used to produce credible “deep fakes” before the election, the discussants raised an issue I had not considered. They predicted that AI will make political messages of all types so easy to produce that most people will simply be overwhelmed by messages and find it difficult to sort through this barrage to make meaningful personal decisions. I do have advice to offer on this issue (return to RSS and follow those you trust), but I will save longer advocacy for a future post.

The issue of being overwhelmed by content is not new, but the quantity is ramping up and AI content generation will likely make this trend even more significant. This post addresses this issue from the perspective of a content creator. I author three specific blogs with two focused on technology in education and one more general content. While I believe I have a credible background for commenting on technology in education and making recommendations and creating tutorials, when are my expertise and experience really that valuable? For example, why should a learner follow bloggers or search for relevant content when AI sources will generate credible tutorials and discuss this content with you when requested? I recognize that AI can drift off course and a tool such as ChatGPT is not a good source for recent topics because it was trained a year or so ago, but for established, factual content, AI does a credible job.

I do not intend to quit writing, but thinking about the issue of relevance is probably important. I do think there is still a role for experience and expertise. I also think there is an important role for analysis. Factual content should be easy for AI (yes I can point to specific examples of errors. Last night I asked BARD to provide a description of Target Field while I was at a Twins game. The service did a great job, but claimed that Target Field has a retractable roof which I can guarantee it does not have.), but when there is a substantial body of content for an AI service to access, factual errors will become rare for topics that generate consistent descriptions. 

Here is one observation that may be relevant. I find the data generated by my blogs interesting to consider. Which posts generate the most interest? Again, I think I have the credentials to write authoritatively about issues related to technology use in teaching and learning. However, the most popular post I have written by far was and continues to be about a visit my wife and I made to the Amish Greenhouses of southern Minnesota. I wrote this post in 2019 and I have comment on related issues since (what about photographing the Amish) and even this year this post remains the most popular thing I have written.

Why? I can only guess, but I think this post filled a unique niche providing information on a topic that continues to be difficult to find elsewhere. The Amish be a function of their personal beliefs are unlikely to use online sources to advertise their business ventures. You can find simple listening, but very little of any depth including images of just what these greenhouses are like. Gardeners in the Spring are interested in inexpensive places to purchase quality plants and the lure of visiting a location allowing interaction with people with a very different world view is an added benefit. As the weekends this time of year approach, the hit count of this post (and followups) jump dramatically. One of the more recent posts included a photo of the hand drawn map showing the farms with greenhouses and this evidently also was of significant value.

So? I guess I write because I like to write and the process teaches me interesting things about myself and the world. Sometimes, I hit on a topic that evidently provides a similar experience for others. 

We just returned from our annual trip to the Amish Greenhouses and I can’t resist including a few pictures. I always ask permission when I take photos that could potentially include people. The farmer who arrived to restock one 

I am a serious gardner and I start the seeds for my vegetable plants inside before transplanting. I am interested in how greenhouses go about their busness. I asked Google Bard about a specific topic that interested me. Because Bard is sometimes regarded as factually suspect, let me offer my personal observation on the question I asked. The vegetable plants sold by the Amish appear to be started at the greenhouses from seed. The flowers are either started from plugs or purchased as potted plants from greenhouse wholesalers.

Do the Amish raise their plants from seed?

The Amish do raise plants in their greenhouses from seeds. They also sell plants that they have propagated from other plants. In some cases, they may also buy plants from other growers.

The Amish are known for their commitment to quality and sustainability. They use only natural methods to grow their plants, and they do not use any pesticides or herbicides. They also take great care in selecting the seeds that they use.

The Amish greenhouses in Minnesota are a great place to find high-quality plants at an affordable price. The plants are grown with care and attention, and they are sure to thrive in your home or garden.

Here are some more details about how the Amish raise plants in their greenhouses:
They use only natural methods to grow their plants.
They do not use any pesticides or herbicides.
They take great care in selecting the seeds that they use.
The plants are grown with care and attention.
They are sure to thrive in your home or garden.

If you are looking for high-quality plants at an affordable price, I encourage you to visit an Amish greenhouse in Minnesota. You won’t be disappointed.

133 total views