AI – Page 2 – Learning Aloud

Trust AI?

I have found that I cannot trust AI for a core role in the type of tasks I do. I am beginning to think about why this is the case because such insights may have value for others. When do I find value in AI and when do I think it would not be prudent to trust AI?

I would describe the goal of my present work or hobby, depending on your perspective, as generating a certain type of blog post. I try to explain certain types of educational practices in ways that might help educators make decisions about their own actions and the actions they encourage in their students. In general, these actions involve learning and the external activities that influence learning. Since I am no longer involved in doing research and collecting data, I attempt to provide these suggestions based on my reading of the professional literature. This literature is messy and nuanced and so are learning situations so there is no end to topics and issues to which this approach can be applied. I do not fear that I or the others who write about instruction and learning will run out of topics.

A simple request of AI to generate a position on an issue I want to write about is typically not enough. Often a general summary of an issue AI generates only tells me what is a common position on that topic. In many cases, I agree with this position and want to generate a post to explain why. I think I understand why this difference exists. AI works by creating a kind of mush out of the content it has been fed. This mush is created from multiple sources differing in quality which makes such generated content useful for those not wanting to write their own account of that topic. I write a lot and sometimes I wish the process was easier. If the only goal was to explain something that was straightforward and not controversial relying on AI might be a reasonable approach or at least a way to generate a draft.

As I said earlier, educational research and probably applied research in many areas is messy. What I mean by that is that studies of what seems to be the same phenomenon do not produce consistent results. I know this common situation leads some in the “hard” science to belittle fields like psychology as not a real science. My response is that chemists don’t have to worry that the chemicals they mix may not feel like responding in a given way on a given day. The actual issue is that so many phenomena I am interested in are impacted by many variables and a given study can only take so many of these variables into account. Those looking to make summary conclusions often rely on meta-analyses to combine the results of many similar studies to achieve a type of conclusion and this approach seems somewhat similar to what AI accomplishes. Finding a general position glosses over specifics.

Meta-analysis does include some mechanisms that go beyond the basic math involved in combining the statistical results of studies. This approach involves the researchers looking for categories of studies within the general list of studies that identify a specific variable and then quantitatively or logically trying to determine if this unique variable modified the overall result in some way.

The approach of examining subcategories is getting closer to what I am trying to do. I think it essential when considering an applied issue to review the methodology of the studies that differ and see what variables have been included or ignored. There is not an easy way to do this. It is not what AI does and it is not something humans can do when simply reviewing the abstracts of research. Do the researchers control a variable you as a consumer/practitioner think may matter? I encounter this issue frequently and I admit this experience often occurs because I have a bias that trends in a different direction than what the data and conclusion of a given study may propose. Biases are always important to recognize, but science relies heavily on doubt and testing alternatives is an important part of the process.

For example, I don’t happen to believe that taking notes by hand and reading from paper are necessarily better than their digital equivalents. I have read most of the studies that seem to make this case, but I find little in the studies that would explain why? Without credible answers to the “why” question, I continue to doubt and since I cannot generate data myself, I continue to examine the data and methodologies of specific studies looking for explanations.

Long intro, but I thought it necessary to support the following point. AI is not particularly helpful to me because conclusions reached from a mess or amalgam of content without specific links to sources I can examine seems a digital representation of the problem I have just outlined. AI searches for a common position when the specifics of situations and may create a common position that is misleading.

If you use AI and haven’t tried this, I would ask that you try working backward. By this, I mean ask the AI system to offer sources that it used in responding to your request. Exploring this approach works best when you know the field and have a good handle on what the AI should produce.

Here is an example I generated recently when trying to understand what factors were responsible for the proposed value of the Cornell note-taking system. Many who read my posts probably recognize this approach to taking notes. What about this system explains why it seems to help students? I was using the AI system Leo which is now available as a tool within the Brave browser.

The following image shows Leo as a window that opens in a Brave side panel (panel is opened using the icon at the top and Leo is activated by the icon enclosed by the red box on the right). At that point, you can being “chatting” with Leo.

Leo gave me a nice analysis of what the Cornell note-taking system is and offered several possibilities for why the system is effective. I then asked Leo for references in support of the reasons offered for effectiveness which resulted in names and dates of studies and I then asked for full citations. Leo responded with the following list and even indicated that it hoped this information would be helpful.

Here are the full citations for the references I provided earlier:

1. Fisher, R. P. (1993). The Cornell note-taking system: A study of its effectiveness in improving lecture note-taking and recall. Journal of Educational Psychology, 85(2), 278-286.
2. Kiewra, K. A. (1994). The Cornell note-taking system: A review of research on its effectiveness. Journal of Learning Disabilities, 27(3), 171-180.
3. Koech, J. (2013). The effect of the Cornell note-taking system on the academic performance of students with learning disabilities. Journal of Learning Disabilities, 46(2), 148-158.
4. Kornell, N., Son, L. K., & Tai, K. (2007). The importance of retrieval for learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(3), 576-585.
5. Bjork, E. L., & Bjork, R. A. (1992). A new theory of disuse and an old theory of stimulus decay. In A. Healy & R. Brown (Eds.), The psychology of learning and motivation: Vol. 8. Cognition and practice (pp. 115-145). New York: Academic Press.

I hope these references are helpful for you! Let me know if you have any other questions.

Here is the thing about this list. None of these citations is real. The names are appropriate as researchers who work in this area, the titles make sense, and the journals exist. However, try inserting any title from this list in Google Scholar and you will find there are no matches. I tried the more direct approach of actually going to the journals and looking for the papers. There was nothing to find. AI mushes things together and loses track of specifics. Even the names and titles make sense out of this mush, but the origins of the information cannot be retraced and reviewed.

If I were to offer the summary of my request as a blog post, it would be informative and accurate. If I were to append the citations on which this summary was generated, I would find myself embarrassed as soon as someone decided they wanted to use a citation to learn more. Is there value here? I think so as long as a user understands what they are getting. AI seems to do a reasonable job of presenting a summary of what others have written. However, at least within the scenario I have described, it is important to understand limitations. When I challenged Leo on a specific citation, Leo was willing to explain in its own words that it had just made the citation up.

I have come to my own strategy for using AI. I use a tool such as Elicit to identify citations that I read creating my own notes. I then use AI tools to offer analysis or summaries of my content and to identify the notes that were used in generating responses. If it references one of my notes, I am more confident I agree with the associated statement.

This post is already far too long, so here is a link to an earlier post describing my use of Obsidian and AI tools I can focus on my own notes.

Why is tutoring effective?

We know that tutoring is one of the most successful educational interventions with meta-analyses demonstrating the advantage to be between .3 and 2.3 standard deviations. In some ways, the explanation of this advantage seems obvious as it provides personal attention that cannot be matched in a classroom. The challenges in applying tutoring more generally are the cost and availability of personnel. One of my immediate interests in the AI tools that are now available is in exploring how students might make use of these tools as a tutor. This is different from the long-term interest of others in intelligent tutoring systems designed to personalize learning. The advantage of the new AI tools is that these tools are not designed to support specific lessons and can be applied as needed. I assume AI large language chatbots and intelligent tutoring will eventually merge, but I am interested in what students and educators can explore now?

My initial proposal for the new AI tools was to take what I knew about effective study behavior and know about the capabilities of AI chatbots and suggest some specific things a student might do with AI tools to make studying more productive and efficient. Some of my ideas were demonstrated in an earlier post. I would still suggest interested students try some of these suggestions. However, I wondered if an effort to understand what good tutors do could offer some additional suggestions to improve efficiency and move beyond what I had suggested based on what is known about effective study strategies. Tutors seem to function differently from study buddies. I assumed there must be research literature based on studies of effective tutors and what it is that these individuals do that less effective tutors do not. Perhaps I could identify some specifics a learner could coax from an AI chatbot.

My exploration turned out to be another example of finding that what seems likely is not always the case. There have been many studies of tutor competence (see Chi et al, 2001) and these studies have not revealed simple recommendations for success. Factors such as tutor training or age differences between tutor and learner do not seem to offer much as whatever is offered as advice to tutors and what might be assumed to be gained from experience do not seem to matter a great deal.

Chi and colleagues proposed that efforts to examine what might constitute skilled tutoring begin with a model of tutoring interactions they call a tutoring frame. The steps in a tutoring session were intended to isolate different actions that might make a difference depending on the proficiency with which the actions are implemented.

Steps in the tutoring frame:

(1) Tutor asks an initiating question

(2) Learner provides a preliminary answer

(3) Tutor gives confirmatory or negative feedback on whether the answer is correct or not

(4) Tutor scaffolds to improve or elaborate the learner’s answer in a successive series of exchanges (taking 5–10 turns)

(5) Tutor gauges the learner’s understanding of the answer

One way to look at this frame is to compare what is different that a tutor provides from what happens in a regular classroom. While steps 1-3 occur in regular classrooms, tutors would typically apply these steps with much greater frequency. There are approaches classroom teachers could apply to provide these experiences more frequently and effectively (e.g., ask questions and pause before calling on a student, make use of student response systems allowing all students to respond), but whether or not classroom teachers bother is a different issue from whether effective tutors differ from less effective tutors in making use of questions. The greatest interest for researchers seems to be in step 4. What variability exists during this step and are there significant differences in the impact identifiable categories of such actions have that impact learning?

Step 4 involves a back-and-forth between the learner and tutor that goes beyond the tutor declaring the initial response from the learner as correct or incorrect. Both teacher and learner might take the lead during this step. When the tutor controls what unfolds, the sequence that occurs might be described as scaffolded or guided. The tutor might break the task into smaller parts, complete some of the parts for the student (demonstrate), direct the student to attempt a related task, remind the student of something they might not have considered, etc. After any of these actions, the student could respond in some way.

A common research approach might evaluate student understanding before tutoring, identify strategy frequencies and sequence patterns during a tutoring session, evaluate student understanding after tutoring, and see if relationships can be identified between the strategy variables and the amount learned.

As I looked at the research of this type, I happened across a study that applied new AI not to implement tutoring, but to search for patterns within tutor/learner interaction (Lin et al., 2022). The researchers first trained an AI model by feeding examples of different categories identified within tutoring sessions and then attempted to see what could be discovered about the relationship of categories within new sessions. While potentially a useful methodology, the approach was not adequate to account for differences in student achievement. A one-sentence summary from that study follows;

More importantly, we demonstrated that the actions taken by students and tutors during a tutorial process could not adequately predict student performance and should be considered together with other relevant factors (e.g., the informativeness of the utterances)

Chi and colleagues (2001)

Chi and colleagues offer an interesting observation they sought to investigate. They proposed that researchers might be assuming that the success of tutoring is somehow based on differences in the actions of the tutors and look for explanations in narratives based on this assumption. This would make some sense if the intent was to train or select tutors.

However, they propose that other perspectives should be examined and suggest the effectiveness of tutoring experiences is largely determined by some combination of the following:

the ability of the tutor to choose ideal strategies for specific situations. (Tutor-Centered)
the degree to which the learner engages in generative cognitive activities during tutoring in contrast to the more passive, receptive activities of the classroom (Learner-Centered), and
the joint efforts of the tutor and learner. (Interactive)

In differentiating these categories, the researchers proposed that in the learner-centered and interactive labels, the tutor will have enabled an effective learning environment to the extent that the learner asks questions, summarizes, explains, and answers questions (learner-centered) or interactively as the learner is encouraged to interact by speculating, exploring, continuing to generate ideas (interactive).

These researchers attempted to test this three-component model in two experiments. In the first, the verbalizations of tutoring sessions were coded for these three categories and related to learning gains. In the second experiment, the researchers asked tutors to minimize tutor-centered activities (giving explanations, providing feedback, adding additional information) and instead to invite more dialog – what is going on here, can you explain this in your own words, do you have any other ideas, can you connect this with anything else you read, etc. The idea was to compare learning gains with tutoring sessions from the first study in which the tutor took a more direct role in instruction.

In the first experiment, the researchers found evidence for the impact of all three categories of tutor session benefits, but codes for learner-centered and interactive had benefits for performance outcomes relying on deeper learning. The second experiment found equal or greater benefits for learner-centered and interactive events when tutor-focused events were minimized.

The researchers argued that tutoring research that focuses on what tutors do may have yet to find much regarding what tutors should or not do may be disappointing because the focus should be on what learners do during tutoring sessions. Again, tutoring is portrayed as a follow-up to classroom experiences so the effectiveness of experiences during tutoring sessions should be interpreted given what else is needed in this situation.

A couple of related comments. Other studies have reached similar conclusions. For example, Lepper and Woolverton (2002) concluded that tutors are most successful when they “draw as much as possible from the students” rather than focus on explaining. The advocacy of these researchers for a “Socratic approach” is very similar to what Chi labeled as interactive.

One of my earlier posts on generative learning offered examples of generative activities and proposed a hierarchy of effectiveness among these activities. At the top of this hierarchy were activities involving interaction.

Using an AI chatbot as a tutor:

After my effort to read a small portion of the research on effective tutors, I am more enthusiastic about the application of readily available AI tools to the content to be learned. My post which I presented more as a way to study with such tools, could also be argued as a way for a learner to take greater control of a learner/AItutor session. In the examples I provided, I showed how the AI agent could be asked to summarize, explain at a different level, and quiz the learner over the content a learner was studying. Are such inputs possibly more effective when a learner asks for them? There is a danger that a learner does not recognize what topics require attention, but an AI agent can be asked questions with or without designating a focus. In addition, the learner can explain a concept and ask whether his/her understanding was accurate. AI chats focused on designated content offer students a responsive rather than a controlling tutor. Whether or not AI tutors are a reasonable use of learner time, studies such as Chi, et al. and Lepper et al. suggest that more explanations may not be what students need most. Learners need opportunities that encourage their thinking.

References:

Chi, M. T., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive science, 25(4), 471-533.

Fiorella, L., & Mayer, R. (2016). Eight Ways to Promote Generative Learning. Educational Psychology Review, 28(4), 717-741.

Lepper, M. R., & Woolverton, M. (2002). The wisdom of practice: Lessons learned from the study of highly effective tutors. In Improving academic achievement (pp. 135-158). Academic Press.

Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gaševi?, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–to–human tutorial dialogues. Future Generation Computer Systems, 127, 194-207.

Digital for serious reading tasks

I keep encountering colleagues who disagree with me on the value of relying on digital content (e.g., Kindle books, pdfs of journal articles) rather than content they collect on paper. I agree that their large home and office libraries are visually attractive and their stuffed chair with reading lamp looks very inviting. They may even have a highlighter and note cards available to identify and collect important ideas they encounter. A cup of coffee, some quiet music in the background, and they seem to think they are set to be productive.

I have only one of my computers, a large monitor when working at my desk, and a cup of coffee. The advantages I want to promote here are related to my processing of content I access in a digital format. What you can’t see looking at my workspace whether it happens to be located in my home or at a coffee shop is the collection of hundreds of digital books and the hundreds of downloaded pdfs I have collected and can access from any locate when I have an Internet connection. I can work with digital content from my home office without a connection, but I prefer to have a connection to optimize the use of the tools that I apply.

For me, the difference between reading for pleasure and reading for productivity is meaningful. I listen to audiobooks for pleasure. I guess that is a digital approach as well and it functions whether in my home, on a walk, or in the car. For productivity, I read to take in information I think useful to understand my world and to inform my writing about topics mostly related to the educational uses of technology. I think of reading as a process that includes activities intended to make available the ideas that I encounter in what I read and in my reactions to this content in the future. Future uses frequently but not exclusively now involve writing something. At one point when I was a full-time educator I engaged in other additional forms of communication, but in retirement, I mostly write.

My area of professional expertise informs how I work. I studied learning and cognition with an emphasis on individual differences in learning and the topics of notetaking and study behavior. One way to explain my present preoccupations might be to suggest I am now interested in studying and notetaking to accomplish self-defined goals to be pursued over an extended period of time. Instead of preparing to demonstrate what I know about topics assigned to me and with priorities established by someone else with the time span of a week or at most a couple of months, I now pursue general interests of my own preparing to take on tasks I can only describe in vague terms now but tasks that may become quite specific in a year or more. How do I accumulate useful information that I can find and interpret when a specific production goal becomes immediate? The commitment I have made to consume and process digital content is based on these goals and insights.

What follows identifies the tools I presently use, the activities involved as I make use of each tool, and the interconnections among these tools and the artifacts I use each tool to produce.

Step 1 – reading.

In my professional work, I made a distinction between reading and studying. This was more for theoretical and explanatory reasons because most learners do not neatly divide the two activities. Some read a little, reread, and take notes continuously. Some read and then read again assuming I guess that a second reading accomplishes the goals of what I think of as studying. Some read and highlight and review their highlights at a later time. There are many other possibilities. I think of reading as the initial exposure to information much like listening to a lecture is an initial exposure to information. Anything that follows the initial exposure, even if interspersed with other periods of initial exposure, is studying.

My tools – Present tools/services related to this stage of processing – Kindle for books and Highlights (Mac app) for PDFs. Other tools are used for content I find online, but most of my actual productive activity focuses on books and journal articles

Step 2 – initial processing (initial studying)

While reading, I use highlighting to identify content I may later find useful and I take notes (annotation as these notes are connected to the book or pdf). Over time, I found it valuable to generate more notes. Unlike the highlights, the notes help me understand why content I found interesting at the time of reading might have future usefulness.

My tools – Present tools/services related to this stage of processing – Kindle for books and Highlights (Mac app) for PDFs. Yes, these are the same tools I identified in step 1. However, the integration of these dual roles is accurate as both functions are available within the same tools. One additional benefit of reading and annotating using the same tool and applied to the same content is the preservation of context. The digital tools I use can be integrated in ways that allow both forward and backward connections. If at a later stage in the approach I describe I want to reexamine the context in which I identified an idea, I can move between tools in an efficient way.

Step 3 – delayed processing (delayed studying)

Here I list tools I would use for accepting the highlights and notes output from Step 2 as isolated from the original text. I also include tools I would use for reworking notes to make them more interpretable when isolated from context, adding tags to stored material, adding links to establish connections among elements of information, and initial summaries written based on other stored information. I like a term I picked up from my reading of material related to what has become known as personal knowledge management (PKM). A smart note is a note written with enough information that it will be personally meaningful and would be meaningful to another individual with a reasonable background at a later point in time.

My tools – Readwise to isolate and review highlights and notes from Kindle. Obsidian to store highlights and notes, add annotations to notes, create links between notes, and generate some note-related summaries an comments. I use several AI extensions within Obsidian to “interact” with my stored content and draft some content. Presently, I use Smart Connections as my go-to AI tool. I write some more finished pieces in Google Docs.

Step 4 – sharing

As a retired academic, I no longer am involved in publishing to scientific journals or through textbook companies. My primary outlet for what I write are WordPress blogs I post through server space that I rent (LearningAloud). A cross-post a few of my blog entries to Substack and Medium.

My tools – I write in Google docs and then copy and paste to upload content to the outlets I use.

Obsidian as the hub

Here are a couple of images that may help explain the workflow I have described. The images show how Obsidian stores the input from the tools used to isolate highlights and notes from full-text sources.

As notes are added to Obsidian, I organize them into folders. An extension I have added to Obsidian creates an index of the content within each folder, but at some point the volume of content is best explored using search.

Here is what a “smart note” I have created in Obsidian looks like. The idea of a smart note is to capture an idea that would be meaningful at a later date without additional content. Included in this note is a citation for the idea, tags I have established that can be used to find related material and a link.

This is an example of a “note” Obsidian automatically generated based on book notes and highlights sent by Readwise. I can add tags and links to this corpus of material to create connections I think might be useful. The box identifies a link stored with the content that will take me back to Kindle and the location of the note or highlight. These links are useful for recovering the original context in which the note existed.

Here is a video explaining how my process works.

So, the argument I am making here is that digital tools provide significant advantages not considered in single-function comparisons between paper and screen. Digital is simply more efficient and efficacious for projects that develop over a period of time.

Applying AI to Discuss Your Own Content

I have moved past the initial stage of awe in connection with access to large language models such as ChatGPT and after considerable exploration have begun to focus on how I might find value in what these systems can provide. I presently use AI tools to support the research I do to inform my writing – blog posts such as this. I have found that I feel uncomfortable trusting a tool like ChatGPT when I simply prompt it to provide me information. There are simply too many situations in which it generates replies that sound good, but are fabrications when checked.

The one task most trustworthy requires that I focus the AI on a source of content I control and can use to check if something seems off. In this post, I will identify three such tools and explain a little of how you might also find these tools helpful.

ChatPDF

As the name implies, ChatPDF allows a user to interact with the content of a designated PDF. Much of the content I personally review consists of scientific journal articles available to me as PDFs from my university library. This has been the case now for many years and I have a collection of hundreds of such files I have read, highlighted, and annotated. The link I provide above explains how ChatPDF allows me to explore the content of content in such files. Because I read and annotate such files anyway, I actually don’t interact with journal articles in this way very often. The link I have provided describes the use of ChatPDF as a tutor applied to a textbook chapter. The intent of the description was to describe multiple ways in which ChatPDF could benefit a learner trying to understand and store important ideas from a document.

The other two examples here describe AI tools available to allow a user to interact with collections of notes. One tool works with notes saved in Obsidian and the second with notes in Mem.AI. These are digital tools for storing and organizing personal notes and digital content. The tools are designed for the organization and exploration of such notes, but as AI has become available new ways to make use of what can become large collections of information can also be applied.

Smart Chat Obsidian Plugin

I have prepared a video to offer some idea of how Smart Chat prompts can be applied to the content stored in Obsidian. If you are unfamiliar with Obsidian, the video also offers a glimpse of Obsidian itself. One point I think is important Obsidian and differentiates it from Mem.AI is the way it stores content. Obsidian stores content as individual text files which include content as text and what is called markdown. Markdown is a simple set of tags that allow a user to establish links, tags, and text embellishments such as bolding, size, etc. The benefit is the independence of the files from any specific tool. So, if Obsidian was to go away, you would still have a collection of documents with your information you could salvage. In contrast, Mem.AI stores content in what I think of a database in the cloud. There are ways to export your content, but I find value in knowing I have my content on my computer in a format I can access if necessary.

The Smart Chat plugin requires that you have an Open.Ai account and add some money to cover the cost of your AI activity. I don’t consider the investment that large and have been contributing $5 a month which has covered the cost of my activity.

Mem.AI

This image shows the home screen for Mem.AI. At the top of the screen, you can see the AI prompt window. I pay $10 a month for MEM.AI, but the AI feature is included. The following prompt (same as I used for the Smart Chat plugin in Obsidian) generated the following response. The content of my two note systems is similar, but not completely identical. You see some differences in the content that was generated.

Prompt

List my notes that involve a comparison of taking notes by hand and by using a keyboard. Write a 400 word summary of these notes.

Response

Based on your knowledge base, there are two notes that involve a comparison of taking notes by hand and using a keyboard:

Horbury2021: This study compared the performance of 10 and 11-year-olds taking notes by hand or using a computer. The study found that conceptual understanding was superior when taking notes by hand. The authors speculated that keyboarding may have produced greater cognitive load, especially for children who may have less experience in keyboarding.
Writing a paper step by step: This note mentions a study by Mueller and Oppenheimer (2014) that found no difference in terms of the number of facts students were able to remember whether they took notes by hand or by typing them into their laptops. However, in terms of understanding the content of the lecture, the students who took their notes by hand performed much better.

Here’s a 400-word summary of these notes:

The method of note-taking, whether by hand or keyboard, can significantly impact a student’s understanding and recall of the material. A study by Horbury (2021) compared the performance of 10 and 11-year-olds taking notes by hand or using a computer. The study found that students had a superior conceptual understanding when taking notes by hand. The authors speculated that keyboarding may have produced greater cognitive load, especially for children who may have less experience in keyboarding. This suggests that the physical act of writing might help students process and understand the information better.

On the other hand, a study by Mueller and Oppenheimer (2014) found no difference in terms of the number of facts students were able to remember whether they took notes by hand or by typing them into their laptops. However, in terms of understanding the content of the lecture, the students who took their notes by hand performed much better. This suggests that while the amount of content stored may not significantly differ between handwriting and keyboarding, the process of handwriting might lead to a deeper understanding of the content.

These findings have several implications for educational practices. Given that handwriting seems to lead to a deeper understanding of the content, educators might want to encourage students to take notes by hand, especially during complex lectures or discussions. However, it’s also important to remember that what works best might vary from student to student. Personalization of learning strategies, taking into account individual strengths and preferences, could also be a valuable approach.

In conclusion, while technology has its place in the classroom, these findings suggest it should not completely replace traditional methods. A balance between digital and analog methods could be the key to effective learning. Furthermore, students could benefit from explicit instruction on effective note-taking strategies, such as focusing on understanding over verbatim transcription. The cognitive load of keyboarding for younger students or those less familiar with typing should also be considered when deciding on note-taking methods.

Summer – time for relaxed exploration

Summer is a great time for educators to do a little reading and explore a bit in preparation for next Fall. Our grandkids are either out of school or will be within a week. The educators in our family are soon to have more time. I usually offer some suggestions for technology-related and classroom relevant summer explorations at about this time of the year.

I seem to be spending so much of my time lately exploring and writing about AI. It is hard to get away from this topic and the uncertainty related to applications and challenges. Everything about AI seems mysterious and as a consequence, unsettling. As I have written previously, I have been unable to find a book that provided the insights I felt I needed and my related recommendation was to explore a combination of personal experimentation and online blog posts and resources as most productive. What follows are recommendations based on this perspective.

I have divided my recommendations based on two goals. First, I want to understand a bit about how AI works and to understand general “how to do it” skills. I don’t like the feeling of not understanding how things work the way they do. Without some sense of understanding, I have trust issues. At the other extreme, I want specific recommendations I can implement. I want examples and variations on these examples I can apply to content and topics of my choosing.

Second, I want specifics related to applications in education.

Here are some recommendations related to the first goal. The content is free with the exception of the Udemy course which I have found useful. I tend to differentiate Google Bard applications from OpenAI applications in my explorations. It is worth spending some time with each, but because I have decided to use several OpenAI API applications (applications built on the model that AI approach used in ChatGPT) I pay to use, I am more experienced and have spent more time with OpenAI-related resources. Hence, I am more confident in these recommendations.

The AI Canon (Andreessen Horowitz)

Generative AI learning path (Google)

ChatGPT complete guide (Udemy – $15?)

As an educator, you may or may not feel the need I feel to invest time in developing a sense of how and why. The following are sources specific to education. The resource from the Office of Educational Technology focuses on AI in education, but lacks the specifics I want. It is a reasonable overview of the potential of AI in education. I am also somewhat put off by the constant emphasis on the message that AI will not replace teachers and humans must remain in the loop, which I find obvious and unnecessary if there is a successful focus on useful applications. It seems there is a concern that those who would read the document in the first place need to be convinced.

I have included one blog post I wrote a couple of months ago. I added it because it is the type of effort I want to read because of the focus on how AI might be used for a specific educational goal. I cannot evaluate the quality of this offering, but I think efforts concerning concrete uses educators can try and/or assign now are the type of thing educators are looking for. I don’t believe in recipes, but my effort was intended to focus on opportunities to address a need and to encourage exploration. I think we are at this stage with AI use in classrooms and the summer is a good time to explore.

Artificial intelligence and the future of teaching and learning (Office of Educational Technology)

Google Bard for educators (Control Alt Achieve)

AI tutoring now (me)

ChatPDF as tutor

Educators concerned about AI and unable to generate productive ways their students could use AI tools need to check this out. The tool is called ChatPDF and is available using a browser or an iPad. At this point, it is free and available without an account.

Once connected you upload a pdf. I wanted to give it a significant challenge and something I could evaluate easily for accuracy so I took a chapter I had written (the chapter on learning as applied to technology from the textbook I wrote with my wife (Integrating technology for meaningful learning) and uploaded it as a pdf file. I then began to ask for explanations, examples, and questions relevant to that chapter. I responded to the questions the AI tool generated and had my answers evaluated. What I have long thought potentially valuable AI was the role AI might play in functioning as a tutor. How can learners get flexible assistance when studying that they can shape to their needs? How can students discover what their needs are and then have their challenges addressed?

While the system did require that I restart a couple of times, perhaps because I was working from a coffee shop with a sketchy connection, I was very impressed with the quality of the system. By quality, I was primarily interested in the accuracy of the content. Were the explanations accurate and different enough from the wording in the chapter to offer a reasonable opportunity for a learner to achieve a better understanding? Were the questions posed more than simplistic keyword vocabulary checks? Was the system flexible enough to understand me even when I got a little sloppy?

Any educator should evaluate similar issues for themselves using material they might assign. I understand that content they might like to evaluate may not be available in pdf format, but as I understand the developers there is already a Google docs version and soon to be a Word version.

There are a few differences between the browser and app versions I observed. The app version references short segments following its replies and the browser version gives a page number. I guess my preference would be the page number as I see value in a learner being able to go back and forth between the book (PDF) and the AI tool. As I have read about this tool there was a warning about the difficulty of the system making connections across different sections of a document and this must apply to transfer/applications external to the document as well. I make no claim that using this AI tool as a tutor is the equivalent of being able to work with a knowledgeable human tutor, but I would argue few students have the advantage of a knowledgeable human tutor at all times.

Take a look. Some example questions and responses the system generated are included in the following images.

The following image may be a little difficult to read, but I was trying to show the text segments the system tells you it primarily used to generate the content it displayed.

Evaluating AI Tools

Like so many other educators, I am trying to decide what I think of AI tools. Granted, the tools produce some reasonable prose. However, how accurate is the content generated? Here is an example.

I recently generated a post focused on the addition of an AI article summarization capability added to MEMEX Garden. The summarization I offered as my example was based on this article describing the new AI capabilities being added to Kahn Academy resources. I understand there are several references here to AI, but the focus should be on the summary of the article. The summary appears below.

Khan Academy has launched a limited pilot of a new learning guide called Khanmigo for educators, powered by GPT-4, the next generation of OpenAI’s large language model technology. GPT-4 is the successor to OpenAI’s GPT-3.5, which ChatGPT is built upon. It can accept images as input, handle up to 25,000 words of text at once, and is designed with more safety and alignment features, among other changes.
Khanmigo is designed to help teachers create personalized learning plans for their students. It uses GPT-4 to analyze student data and provide recommendations for activities and resources that will help them learn more effectively. The tool also includes features like progress tracking and assessment tools to help teachers monitor student progress.
The pilot program is currently limited to a small group of educators, but Khan Academy plans to expand it in the future. The company believes that Khanmigo has the potential to revolutionize education by providing teachers with powerful tools to help them personalize learning for each student.

The summary sounds great, but after reading the article I realized I had a very different interpretation of the focus of the article. By my interpretation, the key capability of the new AI capability of Kahn’s Khanmigo was to engage interactively with students as tutor. Asking about their understanding and then attempting to guide their thinking by asking questions that would encourage them to think in a different way about what they were trying to understand. My interpretation would have suggested that the most important capability was to serve as a tutor to the students more than providing guidance to the teachers.

So, compare my interpretation with the second paragraph I have bolded in the summary provided by the AI. I would encourage your attention to the original article to see if you agree with my assessment.

I copied a few of the statements from the original article to show where I got my interpretation.

“In a blog post, Khan Academy Founder Sal Khan wrote: “When GPT-4 is carefully adapted to a learning environment like Khan Academy, it has enormous potential. It can guide students as they progress through courses and ask them questions like a tutor would. AI can assist teachers with administrative tasks, which saves them valuable time so they can focus on what’s most important — their students.”

I think there is a big difference between arguing that a product helps the student versus helps the teacher simply because these positions mean very different things to me as someone interested in the history of mastery learning and the role of tutors in this instructional approach. Is this quibbling? If my interpretation is correct, I don’t think this is a difference of no consequence.