I use Obsidian plus the plugin Smart Connections to inform my blog writing activities. I write for educational practitioners and academics so I try to carefully base my content on sources that I have read and in many cases intend to cite in the content I generate. With this goal, Obsidian represents an archive I have developed over several years to store and organize notes from hundreds of books, journal articles, and websites. I explore my collection in different ways sometimes seeking notes on a specific article I want to emphasize and sometimes exploring to locate what I have read that is relevant to a topic that I might want to include but perhaps do not recall at the time.
In some cases, I want to use an AI tool to support my writing. I seldom use AI to actually generate the final version of content I post, but I may explore the possible organization of material for something I want to write or I might use an AI tool to generate an example of how I might explain something based on the notes I have made available to the AI tool.
The combination of Obsidian augmented by the Smart Connections plugin allows me to implement a workflow I have found useful and efficient. I have several specific expectations of this system:
I have already read the source material and taken some notes or generated some highlights now stored in Obsidian. I want to write based on this content.
I may not recall relevant sources I have stored in Obsidian because of the passage of time and the accumulation of a large amount of material. I want the AI system to understand my goals and locate relevant content.
I want the AI system to identify specific sources from the content I have reviewed rather than the large body used to train the LLM. I want the system to identify the specific source(s) from this material associated with specific suggestions so that I am aware of the source and can cite a source if necessary.
When a specific source has been identified I want to be able to go directly to the original document and the location within that document that is the location for the note or highlight that prompted the inclusion in the AI content so that I can reread the context for that note or highlight.
Obsidian with the Smart Connections plugin does these things and is to some extent unique because all of the material (the original content) is stored locally (actually within iCloud which functions as an external harddrive) allowing the maintenance of functioning links between the output from Smart Connections, the notes/highlights stored in Obsidian, and the original documents (pdfs of journal articles, Kindle books, web pages).
I do not know for certain that the Obsidian-based approach I describe is the only way to take the approach I take. I am guessing my approach works in part because I am not relying on an online service and online storage. I also use Mem.ai because it allows me to focus on my own content, but linking back to source documents does not work with this service. Mem.ai does include the AI capabilities as part of the subscription fee, but I don’t know when this might be an advantage. The Smart Connections plugin does require the use of an OpenAI API (ChatGPT) and there is a fee for this access.
Example:
Here is an example of what working with the Obsidian/Smart Connections setup is like. I am working on a commentary on the advantages and disadvantages of K12 students having access to AI in learning to write and writing to learn. I propose that writing involves multiple subprocesses and it is important to consider how AI might relate to each of these subprocesses. My basis for the list of subprocesses is based on the classic Flower and Hayes Writing Process Model. I had written a description of the Writing Process Model for a book I wrote and this section of content was stored within Obsidian as well as notes from multiple sources on AI advantages and disadvantages in the development of writing skills. I have not read a combination of the writing process model with ideas about the advantages and disadvantages of AI so this is the basis for what I think is an original contribution.
The following is a screenshot of Obsidian. The Smart Connection appears as a panel on the right side of the display. The left-hand panel provides a hierarchical organization of note titles and the middle panel provides access to an active note or a blank space for writing a new note.
In the bottom textbox of the Smart Connections panel, I have entered the following prompt:
Using my notes, how might AI capabilities be used to improve writer functioning in the different processes identified by the writing process model. When using information from a specific note in your response, include a link to that note.
Aside from the focus of the output, two other inclusions are important. First, there is the request to “use my notes”. This addition is recommended to ensure a RAG (retrieval augmented generation) approach. In other words, it asks the AI service use my notes rather than the general knowledge of the AI system as the basis for the output. The second supplemental inclusion is the request to include a link to that note which is intended to do just what it says – add links I can use to to see where ideas in the output came from.
The output from Smart Connections is in markdown. I copied this output into a new blank note and the links included are now active.
I purposefully selected a note that initially was part of a web page for this final display. I had originally used a tool that allowed the annotation of web pages and then the exporting of the annotated and highlighted content as a markdown file I added to Obsidian. This file included the link from the note file back to the online source. As you can see, the link from Obsidian brought up the web page and with the assistance of the activated service added as an extension to my browser displays what I had highlighted within this web page. Interesting and useful.
Conclusion:
We all have unique workflows and use digital tools in different ways because of differences in what we are trying to accomplish. What I describe in this post is an approach I have found useful and I have included related comments on why. I hope you find pieces of this you might apply yourself.
Here is a new phrase to add to your repertoire – retrieval generated augmentation (RAG). I think it is the term I should have been using to explain my emphasis in past posts to my emphasis on focusing AI on notes I had written or content I had selected. Aside from my own applications, the role for retrieval generated augmentation I envisioned is as an educational tutor or study buddy.
RAG works in two stages. The system first retrieves information from a designated source and then uses generative AI to take some requested action using this retrieved information. So, as I understand an important difference, you can interact with a large language model based on the massive corpus of content on which that model was trained or you can designate specific content to which the generative capabilities of that model will be applied. I don’t pretend to understand the specifics, but this description seems at least to be descriptive. Among the benefits is a reduction in the frequency of hallucinations. When I propose using AI tools in a tutoring relationship with a student, suggesting to the tool that you want to focus on specific information sources seems a reasonable approximation to some of the benefits a tutor brings.
I have tried to describe what this might look like in previous posts, but it occurred to me that I should just record a video of the experience so those with little experience might see for themselves how this works. I found trying to generate this video an interesting personal experience. It is not like other tutorials you might create in that it is not possible to carefully orchestrate what you present. What the AI tool does cannot be perfectly predicted. However, trying to capture the experience as it actually happens seems more honest.
A little background. The tool I am using in the video is Mem.ai. I have used Mem.ai for some time to collect notes on what I read so I have a large collection of content I can ask the RAG capabilities of this tool to draw on. To provide a reasonable comparison to how a student would study course content, I draw some parallels based on the use of note tags and note titles. Instead of using titles and tags in the way I do, I propose a student would likely take course notes and among the tags label notes for the next exam with something like “Psych1” to indicate a note taken during the portion of a specific course before the first exam to which that note might apply. I hope the parallels I explain make sense.
My explorations of AI use to this point have resulted in a focus on two applications – AI as tutor and AI as tool for note exploration. Both uses are based on the ability to focus on information sources I designate rather than allowing the AI service to rely on its own body of information. I see the use of AI to interact with the body of notes I have created as a way to inform my writing. My interest in AI tutoring is more related to imagining how AI could be useful to individual students as they study assigned content.
I have found that I must use different AI services for these different interests. The reason for this differentiation is that two of the most popular services (NotebookLM and OpenAI’s Custom GPTs) limit the number of inputs that can be accessed. I had hoped that I could point these services at a folder of notes (e.g., Obsidian files) and then interact with this body of content. However, both services presently allow only a small number of individual files (10 and perhaps 20) can be designed as source material. This is not about the amount of content as the focus of this post involves using these two services to interact with a single file of 27,000 words. I assume in a year the number of files will be less of an issue.
So, this post will explore the use of AI as a tutor applied to assigned content as a secondary or higher ed student might want to do. In practice, what I describe here would require that a student would have access to a digital version of assigned content not protected in some way. For my explorations, I am using the manuscript of a Kindle book I wrote before the material was converted to a Kindle book. I wanted to work with a multi-chapter source of a length students might be assigned.
NotebookLM is a newly released AI service from Google. The AI prompts can be focused on content that is available in Google drive or uploaded to the service. This service is available at no cost, but it should be understood that this is likely to change when Google is ready to offer a more mature service. Investing time in this service rather than others allows the development of skills and the exploration of potential, but in the long run some costs will be involved.
Once a user opens NotebookLM and creates a notebook (see red box surrounding new notebook), external content to be the focus of user prompts can be added (second image). I linked Notebook to the file I used in preparation for creating a Kindle book. Educators could create a notebook on unprotected content they wanted students to study.
The following image summarizes many essential features used when using NotebookLM. Starting with the right-hand column, the textbox near the bottom (enclosed in a red box) is where prompts are entered. The area above (another red box) provides access to content used by the service in generating the response to a prompt. The large area on the left-hand side displays the context associated with one of the areas referenced with the specific content used highlighted.
Access to a notebook can be shared and this would be the way an educator would provide students access to a notebook prepared for their use. In the image below, you will note the icon (at the top) used to share content, and when this icon is selected, a textbox for entering emails for individuals (or for a class if already prepared) appears.
Once you have subscribed to the monthly payment plan for ChatGPT – 4, accessing the service will bring up a page with the display shown below. The page allows access to ChatGPT and to any custom GPTs you have created. To create a Custom GPT you select Explore and then select Create a GPT. Describing the process of creating a GPT would require more space than I want to use in this post, but the process might best be described as conversational. You basically interact by describing what you are trying to create and you upload external resources if you want prompts to be focused on specific content. Book Mentor is the custom GPT I created for this demonstration.
Once created, a GPT is used very much in the same way a NotebookLM notebook is used. You use the prompt box to interact with the content associated with that GPT.
What follows are some samples of my interactions with the content. You should be able to see the prompt (Why is the word layering used to describe what the designer does to add value to an information source?)
Prompts can generate all kinds of ways of interaction (see a section below that describes what some of these interactions might be). One type I think has value in using AI as a tutor is to have the service ask you a question. An example of this approach is what is displayed in the following two images. The first image describes a request for the service to generate a multiple-choice question about generative activity which I then respond (correctly) and receive feedback. The second image shows the flexibility of the AI. When responding to the question, I thought a couple of the responses could be correct. After I answered the question and received feedback, I then asked about an answer I did not select wondering why this option could not also be considered correct. As you see in the AI reply, the system understands my issue and acknowledges how it might be correct. This seems very impressive to me and demonstrates that the interaction with the AI system allows opportunities that go beyond self-questioning.
Using AI as tutor
I have written previously about the potential of AI services to interact with learners to mimic some of the ways a tutor might work with a learner. I make no claims of equivalence here. I am proposing only that tutors are often not available and an AI system can challenge a learner in many ways that are similar to what a human tutor would do.
This post describes two systems now available that allow learners to work with assigned content that mimics how a tutor might work with a student. Both systems would allow a designer to create a tool focused on specific content that can be shared. ChatGPT custom GPTs require that those using a shared GPT have an active $20 per month account which probably means this approach would not presently be feasible for common application. Google’s Notebooks can be created at no cost to the designer or user, but this will likely change when Google decides the service is beyond the experimental stage. Perhaps the capability will be included in present services designed for educational situations.
While I recognize that cost is a significant issue, my intent here is to propose services that can be explored as proof of concept and those educators interested in AI opportunities might explore future productive classroom applications of AI.
In reviewing the various ways I might use AI, I am starting to see a pattern. There are uses others are excited about that are not relevant to my life. There are possible uses that are relevant, but I prefer to continue doing these things myself because I either enjoy the activity or feel there is some personal benefit beyond the completion of a given project. Finally, there are some tasks for which AI serves a role that augments my capabilities and improves the quality or quantity of projects I am working on.
At this time, the most beneficial way I use AI is to engage an AI tool in discussing a body of content I have curated or created as notes and highlights in service of a writing project I have taken on. There are two capabilities here that are important. First, I value the language skills of an AI service, but I want the service to use this capability only as a way to communicate with me about the content I designate. I am not certain I know exactly what this means as it would be similar to saying to an expert with whom I was interacting tell me about these specific sources without adding in ideas from sources I have not asked you to explore. Use your general background, but use this background only as a way to explain what these specific sources are proposing. What I mean is don’t add in stuff to address my prompt that does not exist within the sources I gave you.
Second, if I ask an AI service about the content I have provided, I want the service to be able to identify the source and possibly the specific material within a source that was the basis for a given position taken. Think of this expectation as similar to the expectation one might have in reading a scientific article to which the author provides citations for specific claims made. My desire here is to be able to evaluate such claims myself. I have a concern in simply basing a claim on the language of sources not knowing the methodology responsible for producing data used as a basis for a claim. For serious work, you need to read more than the abstract. Requiring a precise methodology section in research papers is important because the methodology establishes the context responsible for the generation of the data and ultimately the conclusions that are reached. Especially in situations in which I disagree with such conclusions, I often wonder if the methodology applied may explain the differences between my expectations and the conclusions reached by the author. Human behavior is complex and variables that influence behavior are hardly ever completely accounted for in research. Researchers do not really lie with statistics, but they can mislead by broad conclusions they share based on a less-than-perfect research method. There are no perfect research methods hence the constant suggestion that more research is needed.
Several services approximate the characteristics I am looking for. I will identify three such services. I had hoped to add a fourth, but I intended to subscribe to the new OpenAI applications recently announced, but the $20 a month subscription fee necessary to use these functions was recently suspended so I will have to wait to explore these functions until OpenAI decides to expand the user base.
The three services I have worked with include NotebookLM, Mem.ai, and Smart Connections with Obisidan. I have written about Mem.ai and Smart Connections in previous posts, so I will use NotebookLM for extended comments and then offer quick examples produced by the other two services.
NotebookLM
NotebookLM recently was made available to me so I have had less experience using this tool than the other two I have included.
At this time you can create a notebook based on 5 pdfs or 10 text segments. There is a limit to how much content these units of information can total, but the pdfs I used were research articles so this is a lot of content. In the following image, icons for the 5 pdfs appear in the column on the left of the screen. All of the pdfs have been selected for the interaction I intend with the AI tool and a check beside each pdf indicates this is the case. The window for submitting my prompts appears at the bottom of the image.
The second image shows the results from the prompt I have submitted (I will explain my interest in interacting with these pdfs following this description). The response to this query is provided above the prompt window (I will share the text) and below this window you will see that the summary was based on 10 “citations”. Selecting one of these citations brings up the content that now appears in the left-hand column. This window shows the content of the pdf and some summary comments about that document generated by the AI. The AI also proposes additional prompts a user might want to apply to that content.
The task I asked all three services to address was related to a recent post I wrote describing generative cognitive tasks and comparisons among the impacts of these generative activities. The pdfs were research studies I had reviewed and were the basis for that post. I had detailed familiarity with this content from both reviewing and annotating this material and writing the previous post and I use this approach of first engaging a process without AI to provide a basis for commenting on what the AI provides. The prompt that follows is what I submitted to NotebookLM to see what would be generated. I will use a similar prompt with the other two AI services/
Prompt: Use only my notes, generate a 400 word summary of generative learning activities and include what is known about the relative effectiveness of different activities.
NoteBook response
Generative learning is a type of learning in which students are actively engaged in creating new knowledge by interacting with the material. This is in contrast to passive learning, in which students simply listen to a lecture or read a textbook.
Generative learning activities are designed to encourage students to think critically about the material and to make connections between new information and their existing knowledge.
There is a significant body of research evidence that supports the effectiveness of generative learning activities. A meta-analysis of 500 studies found that generative learning activities resulted in better learning outcomes than traditional, passive learning methods (Coffey et al., 2003).
Some of the most effective generative learning activities include:
Summarizing: Students are asked to summarize a text in their own words. This activity helps students to identify the main ideas of the text and to understand the relationships between them.
Questioning: Students are asked to generate questions about a text. This activity helps students to think critically about the text and to engage with the material in a deeper way.
Discussion: Students are asked to discuss a text with their classmates. This activity helps students to share their understanding of the text and to learn from each other.
The relative effectiveness of different generative learning activities depends on the specific learning goals. For example, summarizing is a good activity for helping students to remember information, while questioning is a good activity for helping students to understand the material in a deeper way.
Generative learning activities can be used in a variety of settings, including K-12 classrooms, higher education, and corporate training. They are a valuable tool for helping students to learn and remember new information in a more effective way.
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You will note in this material that NoteBook included a reference. This was not one of the pdfs I provided for review so I thought it might have been included in the reference section of one of the pdfs. I asked Notebook to provide a full citation and it generated the following.
Coffey, J. E., & McDonald, J. (2003). The effects of generative learning activities on student achievement: A meta-analysis. Contemporary Educational Psychology, 28(4), 240-263. doi:10.1016/j.cedpsych.2003.09.001
While this citation looks authentic and has the proper APA format, it is made up. I tried searching Google Scholar and used my library online access to search the location in Contemporary Educational Psychology and no such article exists. Google asks for user feedback and I provided the fake citation in my evaluation.
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Mem.AI
My use of Mem.ai and Smart Connections require a little different approach. The content stored in these services results from my exporting notes and highlights from the research articles I read. Both services are directed at my entire collection of notes so this is different from what NotebookLM could access. However, the citations indicated that the output was based on very similar source material.
Prompt: Use only my notes, generate a 400 word summary of generative learning activities and include what is known about the relative effectiveness of different activities.
Mem.ai response
Generative learning is a process where learners actively engage with material, creating new knowledge and connections. This approach involves strategies such as summarization, teaching, self-testing, and self-explaining, which require learners to select and organize relevant information, elaborate on the material based on personal knowledge, and integrate new information with existing knowledge 1^.
Fiorella and Mayer (2016) identified eight strategies that promote generative learning, including summarization, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. These strategies help learners to create a product based on existing knowledge 1^.
Brod (2021) focused on age differences in generative learning strategies and emphasized the importance of producing a meaningful product that goes beyond simple paraphrasing or retelling. For instance, highlighting does not result in new content, but summarization does. Brod’s review suggests that summarizing becomes useful from high school onwards, while testing is beneficial for all ages 2^.
Chi’s (2009) framework differentiates learning activities into active, constructive, and interactive categories. Active engagement with the material can activate existing knowledge and store new input, while constructive activities involve creating a product based on existing knowledge. Interactive activities involve engaging with others to produce a product that integrates external input with personal knowledge 1^.
Fiorella (2023) proposed a new framework of sense-making that distinguishes among three sense-making modes—explaining, visualizing, and enacting—that serve unique and complementary cognitive functions. The explaining mode generalizes knowledge, the visualizing mode organizes knowledge, and the enacting mode simulates knowledge 3^.
In conclusion, generative learning involves actively engaging with the material to create new connections and deeper understanding. The effectiveness of different generative learning activities can vary based on factors such as age and the specific learning context. However, strategies such as summarization, teaching, self-testing, and notetaking with revision and elaboration can promote generative learning and enhance understanding of the material.
Summarize my notes on generative learning Tue 4/18
At the end of each paragraph in this output, you see a number. The number corresponds to the citation at the bottom. About the three links that take a user to that note, you see the names of the notes. So, this output is carefully linked to sources. I will provide an example of what my notes look like in association with the output from Smart Connections, but each note contains a citation of the article from which I created that note.
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Smart Connections (within Obsidian)
Obsidian contains my notes from research articles I have read and also highlights from other sources provided to Obsidian through Readwise. I tell you this again because I do not have a comparison among AI services based on exactly the same base of resources. However, again, you will note similar sources are found. Note also that the prompt is a little different to get Smart Connections to include citations.
Prompt: Use only my notes, generate a 400 word summary of generative learning activities and include what is known about the relative effectiveness of different activities. Include links to the individual source notes within your summary
Response:
Based on your notes, generative learning involves actively making sense of new information by mentally reorganizing and integrating it with one’s prior knowledge (Fiorella and Mayer, 2016). It is a constructive process that requires learners to select important information, reorganize it, and relate it to what they already know (Fiorellaetal2016).
Fiorella and Mayer (2016) identified eight strategies that promote generative learning: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Summarizing involves selecting the main ideas, organizing them into a logical structure, and integrating new information with prior knowledge (Fiorellaetal2016). It is more effective for relatively simple concepts and requires training to use effectively (Fiorellaetal2016).
Mapping and drawing also involve transforming information into different forms of representation (Fiorellaetal2016). These strategies help learners build connections and organize the material (Fiorellaetal2016).
Self-testing, or retrieval-based learning, requires learners to actively retrieve information from memory without access to the provided materials (Fiorellaetal2016). It strengthens memory and makes it more accessible in the future (EndresHigh).
Self-explaining involves explaining the content of a lesson to oneself during learning (Fiorellaetal2016). It promotes meaningful learning outcomes when learners engage in reflective knowledge building (Fiorellaetal2016).
Teaching, on the other hand, involves selecting relevant information, organizing it into a coherent structure, and elaborating on it by incorporating existing knowledge (Fiorellaetal2016). It sets itself apart from other generative strategies by focusing on helping others learn (Fiorellaetal2016).
Based on the research by Fiorella and Mayer (2016), it is more appropriate to consider each generative strategy as suitable for certain types of learning and learning contexts rather than regarding one strategy as the most effective (Fiorellaetal2016).
In a study by Koh, Lee, and Lim (2018), the learning benefits of teaching were investigated. The results showed that teaching improved learning outcomes when it involved retrieval practice, but not when learners used a prepared script (Generative > TeachingAsRetrieval). This supports the idea that retrieval practice is an effective generative learning activity.
Overall, generative learning activities have been shown to be effective in promoting meaningful learning outcomes. However, the effectiveness of each activity may vary depending on the complexity of the material and the training provided to learners. It is important to consider the specific learning goals and contexts when selecting and implementing generative learning strategies (Fiorellaetal2016).
Based on your notes, here are the individual notes that provide more information on each generative learning activity:
– [[Generate1]]: Provides an overview of generative learning theory and the eight strategies identified by Fiorella and Mayer (2016).
– [[Summarizing]]: Describes the summarizing strategy and its effectiveness in selecting main ideas and creating quality summaries.
– [[Teaching]]: Discusses the goal of teaching and its distinction from other generative strategies.
– [[Self-Testing]]: Explains the self-testing strategy and its role in retrieval-based learning.
– [[Promote generative learning]]: Provides a link to the article by Fiorella and Mayer (2016) that identifies the eight strategies and discusses their effectiveness.
– [[Fiorellaetal2016]]: Summarizes the key points from Fiorella and Mayer’s (2016) article on generative learning strategies.
– [[EndresHigh]]: Discusses the importance of task differences in retrieval practice and the effects of different types of recall tasks.
– [[Yang2021]]: Highlights a meta-analysis on the effectiveness of retrieval practice in classroom learning and raises questions about the mechanism and student perception of its benefits.
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Note: The output of Smart Connections is in markdown and the terms included in double parentheses are links that connect to the source note. So, if the summary above was opened in an app that would interpret markdown, the text within the double parentheses would appear as a link and the link would take me to a file stored on my computer. The file is named Generate1.
Here is an example of one of the original notes that was identified as source material.
Generative learning makes sense of new information by reorganizing it and relating it to existing knowledge. This position comes from Wittrock, but is similar to other theorists (Mayer, Piaget). This specific article identified eight learning strategies that promote generative learning and provides a review of research relevant to each strategy.
[[Summarizing]]
Mapping
Drawing
Imagining
[[Self-Testing]]
Self-Explaining
[[Teaching]]
Enacting
The first four strategies (summarizing, mapping, drawing, and imagining) involve changing the input into a different form of representation.
The final four strategies (self-testing, self-explaining, teaching, and answering practice questions) require additional elaboration.
Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. _Educational Psychology Review, 28(4), 717-741.
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Summary
Keeping in mind my recognition that the AI of the three AI services was applied to slightly different content, I would argue that Smart Connections and Mem.ai are presently more advanced than NotebookLM. Eventually, I assume a user will be able to direct NotebookLM at a folder of files so the volume of content would be identical. Google does acknowledge that Notebook is still in the early stages and access is limited to a limited number of individuals willing to test and provide feedback. The content generated by all of the services was reasonable, but NoteBook did hallucinate a reference.
My experience in comparing services indicates it is worth trying several in the completion of a given task. I have found it productive to keep both Smart Connections and Mem.ai around as the one I find most useful seems to vary. I do pay to use both services.
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.
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.
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.
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