AI – Learning Aloud

AI (Grammarly) and Writing: Good or Evil?

I am interested in the potential of AI in developing writing skills. I am presently focused on Grammarly, having used the tool for years, and am now considering how it might play a productive role in secondary and higher education efforts to develop writing skills. Exploring what those writing about Grammarly on Medium have to say about Grammarly, I have come away with the impression the majority argue the tool is overpriced, less effective than tools with a similar purpose, and generally a bad idea when applied in an educational setting. I don’t agree.

Writing in the classroom

I like to draw a distinction between learning to write and writing to learn. This distinction is artificial, as classroom instructional strategies such as “Writing Across the Curriculum” argue that both goals can be addressed when writing assignments in other disciplines are evaluated both for the quality of the writing and for what the writing suggests about students’ understanding of a given topic. My argument here focuses on the potential value of AI in learning to write.

When and why is AI a problem when learning to write

In situations where the development of writing skills is the emphasis, an AI tool is argued to be problematic because students cheat by using AI to avoid doing work that requires them to practice the skills they are expected to master. In addition, by turning in work for evaluation that students did not actually perform, they do not receive feedback on the skills they are supposed to be learning and are credited with achievements they have not actually earned.

AI and writing: A different take on the actual problem?

Many educators, aware of the possibility of cheating, have resorted to approaches such as short, handwritten in-class assignments that eliminate the possibility of using AI. There are limitations to this approach, especially for the unique skills required to create longer arguments or other lengthier projects.

As adults with reasons to write and without worrying about the need to prove everything that appears in a written product is based on our own knowledge and writing skills, we may take advantage of AI in many different ways. One real question is how, and perhaps if, we are preparing students to transition from a focus on learning new skills or the graded demonstration of one’s knowledge to a combination of AI and personal knowledge and writing skills. In addition, some are suggesting, and again rightfully so, that AI can benefit students’ efforts to learn to write and write to learn. Here, I want to emphasize the potential assistance in learning to write more effectively.

I tend to react to what I think are naive expectations of teachers and the reality of working in classrooms is important here. For example, I support the exploration of AI as a tutor, not because I think AI is equivalent to a human tutor, but because human tutoring is costly and many students who need help do not receive sufficient human attention as a consequence. I have a similar opinion about learning to write. It would be great if each student could write a lot and receive rapid feedback, as well as an individual conference related to their effort. Neither immediate, consistent feedback nor frequent individual attention is practical. Just having an AI writing tool, such as Grammarly, that can provide immediate feedback on what has been written seems like a practical improvement.

So Much Depends on Personal Motivation

Grammarly and asking pretty much any AI tool to evaluate specific attributes of your writing quality will provide you with feedback to consider. The issue is really whether you take the time to ask for this feedback and to consider the feedback that is produced. Here is what I mean. I use Grammarly while I write, and it constantly provides feedback. In reflecting on my own behavior, I almost always quickly accept the suggestions for what I have written (these appear as underlines in various colors) by clicking to have Grammarly fix the problem. I don’t stop to figure out what was wrong with what I wrote. Was that an actual error of grammar or spelling, and if so, why? The fixes always seem better, but they also remove what may just be my voice or personal preference in how I say something. I avoid the opportunity to learn and also allow Grammarly to “standardize” my writing. At this moment, admitting this has made me self-conscious.

This reminds me of the experience I had providing comments on many of my grad students’ theses and dissertations. In later years, I liked to use the comments feature in Google Docs to leave comments and identify actual errors. I started to realize that some students were simply allowing me to rewrite their papers, when what I wanted was for them to consider something different. Often, I had to remind them of the difference between my thoughts about their work and the actual errors I pointed out.

If you use a tool such as Grammarly, you probably recognize my observation in your own behavior. It is so easy to accept proposed changes based on a kind of “that sounds pretty good thinking” and trust in the assumption that the “system knows the rules better than I.” Taking this approach is quick, painless, and “good enough.” The problem is that this approach fails to take advantage of at least some of these situations to learn. Why were these changes recommended? Is my way of expressing myself flawed or just unique? Grammarly will help you consider which is most likely.

What was wrong with what I wrote?

Grammarly has always allowed you to pause when suggesting a change. There was no time limit on the opportunity to consider what you wrote in comparison to what was recommended. As the tool was improved and with the more recent integration of AI, efforts were made to explain why a change was recommended. At first, the tool offered a general reference to rules. Here is what a split infinitive is, and here are some examples of sentences containing a split infinitive and improved versions of the same sentences. Here is an example of passive voice, and here are some examples. The most recent advance offers similar information, but specifically related to your own words rather than just generic examples.

One note – I have encountered descriptions of this newest capability from others, but I haven’t been able to replicate the same output on my own computer with the latest version of Grammarly. I have had this difficulty even though I input exactly the same text used in the other demonstrations I have encountered. My setup will identify the error and provide generic examples, but it won’t explain based on the text I have entered. I can generate explanations specific to my written text, but I have to use the AI window to enter a prompt asking for this information (see examples below).

Here are a couple of examples. In the first, you see a sentence with three components underlined in blue (I highlighted it in blue so you can find it). In the associated column on the right you see the proposed alternative with the changed words or punctuation bolded. The red box identifies the button to get additional information. The second image shows the result of making use of this button. The explanation for the proposed changes – Our revised version of your text may make it more effective. – is Grammarly’s explanation. As I said, this offers little of value to me. However, if I use the AI prompt box (Grammarly calls this Superhuman Go), which appears at the base of the right-hand column, I can ask for an explanation, and I receive the lengthy explanation shown in the third image.

Here is a second example that explains a split infinitive. Again, if I ask for an explanation related to the limitation in my writing, I get both a general and a targeted explanation of the split infinitive (second image).

Improvements at multiple levels

Improvement in writing happens at multiple levels. There are spelling errors and grammatical failures. These are the easiest for a digital tool to identify, and spelling correction has been around for decades. These might be described as superficial errors. There are deeper levels of good writing that any of us who critique others’ work find much harder to evaluate and that are the most difficult proficiencies to teach and learn. Grammarly’s AI capabilities provide a way to address these proficiencies. The easiest way I can describe this capability is to list some of the prompts Grammarly suggests.

“Critique the logic and reasoning in this text.”
“Are there any gaps in my argument here?”
“Does this conclusion follow from my premises?”
“Point out any weak points in my reasoning.”

I will append Grammarly’s extensive analysis of a recent post based on one of these prompts (see Appendix). I have extensive experience as an academic writer with two published textbooks. These works involved feedback from a dedicated editor and input from multiple paid professors regarding both the content and communication style. The detail in Grammarly’s feedback is easily as detailed.

Summary

The point I am trying to make here is not that AI cannot be used to cheat. My point is that Grammarly and other AI tools can also provide feedback more quickly and consistently than is practical for educators. This seems to be another example of the constant dilemma with AI, it can be used for good or for evil. In this case, when a writer is working on their own, they decide which it will be.

Addendum

Grammarly’s analysis of AI Processing Options.

Prompt: Point out any weak points in my reasoning

A few places in the reasoning could be stronger or more precise:

The continuum needs clearer criteria
You describe a continuum from “total AI” to “total human,” but the dimensions of that continuum are a little mixed. Sometimes the distinction is about who selects the source material, sometimes who reads it, sometimes who organizes it, and sometimes who writes the final text. You might strengthen the piece by naming the dimensions explicitly, such as:

control over source selection
depth of human engagement with sources
human contribution to interpretation
AI role in organization
AI role in final wording

“Information input” may be too narrow
In the Recall.ai example, you say the main thing you control is the information input. But choosing 15 PDFs is not just supplying information; it also frames the argument by deciding what evidence counts. That choice may involve substantial judgment, even if you do not read the documents closely.
The quality question needs more defined standards
You ask which approach produces the highest-quality product, but “quality” could mean many things: factual accuracy, originality, usefulness, writing style, source integration, depth of understanding, efficiency, or ethical acceptability. The argument would be stronger if you noted that different approaches might score differently depending on the quality standard.
The “total AI” end may not be truly total AI
Even in the ChatGPT/Claude example, the human still defines the topic, purpose, and prompt. So it may be more accurate to call this “minimal human input” rather than “total AI.”
The “total human” end still includes tool-mediated thinking
The Obsidian-only example is described as having no AI use, which makes sense if the point is AI involvement. But it still involves a digital tool that shapes organization, retrieval, linking, and thinking. If your continuum is specifically about AI involvement, that is fine—but you may want to clarify that it is not a continuum from tool-free writing to machine-written writing.
The RAG example may understate verification
You note that you do not have to read the documents, but that raises an important issue: without reading them, you may not be able to evaluate whether the AI’s summaries are accurate or whether the final product fairly represents the sources. This is an important weakness in that approach and could be made more central.
The Karpathy Wiki stage could use more explanation
This is the most complex example, but the reasoning around why it is distinct could be clearer. Is the key difference that AI creates a reusable knowledge structure? That it separates source processing from final generation? That it mimics a human note system? Stating the main conceptual difference directly would help.
“Parallel to how human writers function” is promising but underdeveloped
You suggest that identifying themes and concepts before drafting may parallel human writing. That is an interesting claim, but it needs a bit more support. Human writers do not just extract concepts; they evaluate, synthesize, prioritize, and sometimes change direction. You could strengthen this by explaining exactly which parts of the AI process resemble human writing and which do not.
The ethical/authorship issue is implied but not addressed
Your categories raise questions about authorship, responsibility, and acceptable AI assistance, but those issues are not directly discussed. If this is meant to help readers evaluate AI writing practices, you may want to include a brief note about where responsibility for claims and interpretations resides.
The examples could be labeled more consistently
Some category labels name tools, while others name processes. For example, “ChatGPT or Claude” names tools, while “Obsidian to Karpathy Wiki to Product” names a workflow. You might make the structure more analytical by using labels like:

Prompt-only generation
Source-constrained generation
Human-annotated source generation
AI-organized knowledge-base generation
Human-authored synthesis

Overall, the central idea is strong: you are not treating “AI writing” as one thing, but as a range of workflows with different levels of human involvement. The main improvement would be to make the continuum’s criteria explicit so readers can see exactly what changes from one category to the next.

An AI LLM Can Identify Important Missing Content In Student Notes

There seems to be a resurgence of research interest in note-taking and note-use. One source of new work was the controversy over whether students should take handwritten notes or use their laptops or tablets. A second focus has been generated by what I would describe as “out of classroom” notetaking described by those promoting personal knowledge management (PKM) and interactions with a second brain.

I have had a long-term interest in notetaking and studying and recent activity has caused me to review old sources in light of newer activity. Possible connections between notes taken and stored digitally and the capabilities of AI tools seem worth exploring.

I still understand note-taking and note use from a decade old model that argues learning with and from notes can be outlined in terms of an immediate generative benefit of taking notes and by then using the external storage function of notes for review and study. A generative benefit implies that taking notes provides an advantage over simply listening and reading. The external task prompts the note-taker to identify important ideas because it is not practical to record everything, and then cognitively acts on the selected information to personalize the original content through summarization and connections to existing knowledge. Of course, there are many strategies for doing this, as well as individual differences and less active approaches, such as simply writing down what the instructor displays on his/her PowerPoint slides.

One topic relevant to how I imagine AI being used in interaction with student notes is work that examines how effectively learners have recorded important ideas. How many ideas find their way into the notes and are these ideas important ideas? Here you find some interesting controversies. Early work on note-taking in actual classroom settings found a substantial correlation between the volume of content students recorded and their exam performance (Nye and colleagues, 1984). I want to emphasize the word “naturalistic” here because, as the authors of this study claim, so much of the research has been conducted in short-term laboratory studies involving short segments of content and immediate examinations – not the situation students face in practice. I have made the same complaint in a recent post.

A recent controversy related to what might be described as the “more is better” position is the handwriting vs. keyboarding controversy. The issue here is that keyboarding allows for more input, but handwriting appears to lead to more generative activity because the slower process encourages greater selectivity and summarization. My observation is that selection and reworking of notes does not have to happen in real time and having more content to work with during review and study makes such reworking more productive.

This brings me to a second observation. While it seems possible that learners would not record information they think they already know, for one reason or another, students fail to record a sizeable proportion of important information, and what is not recorded can be related to specific later failures of recall and application on learning measures. It is difficult to study what you don’t know and what was not recorded (e.g., Kiewra, 1985; Lawson & Mayer, 2024).

Expert Notes and the AI Alternative

One solution to the many important ideas that students miss, especially less capable students, is to provide what are often called “expert notes” – essentially notes provided by the instructor or taken by an advanced student (Kiewra, 1985). This is a topic I explored in my own research with my graduate students (e.g., Grabe and colleagues, 2005). To be clear, the idea is not to substitute expert notes for student notes because of the generative value of taking notes, but to provide a second source learners can use to “fill in the blanks” for what they missed or perhaps did not understand well enough to even attempt recording.

There are practical issues with providing quality notes to students. One issue that interested me in the citation I provided was the relationship between providing quality notes and class attendance. You do want students to attend class, but the reality is that there is also the question of what can be done for those students who miss class for legitimate reasons. Second, the generative effect of taking notes describes note-taking itself as a learning experience, so you really don’t want to discourage student note-taking.

I have been exploring a use of AI that seems interesting and efficient in a couple of ways. It is relatively easy to prompt AI to identify the “important ideas in a lecture”, to compare this material with the notes taken by a learner, and to generate an output of the important ideas not contained in the student notes. This approach is efficient for students because it does not require reviewing the entirety of what might be considered “expert notes.” It also builds on rather than eliminates the student’s generative effort to create their own notes.

To explore how this might work, I used my current AI service to run the following prompt. The inputs included, first, the transcript of a source podcast on why climate change can be responsible for both drought and flooding, and, second, the notes I took while listening to the podcast. The original source contained approximately 2500 words and as a podcast required approximately 15 minutes. I attempted to play the role of students and created a set of notes while listening. I admit that I was surprised to learn a few things about how climate change works even though the material was prepared at the high school level. I purposely ignored comments that appeared near the end of the podcast that focused on fire danger and health concerns as a way to approximate what notes might look like if a student became distracted or cut out of class early.

Prompt: I am providing two text segments. I want you to identify the important content from the first segment. I then want you to compare this important content with the content in the second segment and identify the important ideas in the first segment that are not included in the second.

I have included, as an Appendix, both the important ideas identified by the AI tool and the list of “important ideas” in the presentation that the AI tool said were not included in my notes.

The approach identified and provided missing comments on both wildfires and health issues, which was my proof of concept when it came to responding to the problem of students missing important ideas, but it also included additional information that the comparison of the original content and my notes did not. So, when I compared the summary with my notes, the comparison in response to the prompt identified content I purposefully missed and other items I heard but did not record. This second body of information was much larger than I would have anticipated and fits existing research concluding even better students (apologies if this sounds conceited) fail to record more than one might expect. It is hard to know if this is an actual problem or simply a difference in standards. I have no idea whether the prompt could be altered to change how the AI tool interpreted “important content.”

One interesting item – there is at least one item in the comparison between the AI summary of important ideas and my notes identified as missing that actually does not appear in the AI summary but was included in the full transcript (The claim that the most destructive floods now occur about three times as often as before).

Final Comments

It is likely not a surprise that AI creates a summary of content, as this is a commonly offered service now often available as an alternative to asking readers or listeners to review all of the original raw content. I am not certain if I explored different wordings of my prompts if a request for the “important ideas” would be much different from a summary.

I have considered whether a summary or review of the most important information would offer essentially the same practical value as the second step I have AI to provide. The amount of content would differ. The summary I provide as an appendix is about a third of what would be generated from a traditional fifty-minute college lecture. I do like the idea of identifying what a student missed, which may have unique value in prompting students to consider their note-taking process. Helping students identify important information they missed also keeps the focus on student responsibility for the generative learning opportunity in making their own notes.

Sources:

Grabe, M., Christopherson, K., & Douglas, J. (2005). Providing Introductory Psychology Students Access to Online Lecture Notes: The Relationship of Note Use to Performance and Class Attendance. Journal of Educational Technology Systems, 33(3), 295-308.

Kiewra, K.A. (1985). Providing the instructor’s notes: an effective addition to student notetaking. Educational Psychologist, 20(1), 33-39. https://doi.org/10.1207/s15326985ep2001_5

Nye, P.A., Crooks, T.J., Powley, M., & Tripp, G. (1984). Student note-taking related to university examination performance. Higher Education, 13(1), 85-97. https://doi.org/10.1007/BF00136532

Appendix:

Important content in Segment 1

Climate change is intensifying both droughts and heavy rainfall/flooding.
A warmer atmosphere holds more water vapor, while hotter land surfaces evaporate moisture faster:
- This dries land and worsens drought.
- It also creates the potential for more intense downpours once rain occurs.
The most serious change may be in extreme storms, rather than in average rainfall or average storms.
Existing infrastructure is poorly suited to extreme weather, especially because roads, roofs, parking lots, and other impervious surfaces increase runoff and flooding.
Flood risk is not limited to coasts and rivers; intense rainfall can cause serious stormwater flooding inland.
Communities can reduce flood impacts through:
- Updated emergency plans and warning systems
- Recovery resources
- Green space, trees, grass, and rain gardens
- Reducing impervious surfaces, including through greater public transportation
Climate impacts vary by region:
- New England’s warming coastal ocean can fuel stronger winter storms.
- Subtropical regions, including the U.S. Southwest, are generally expected to become drier or more arid.
- Some areas may not follow this pattern; for example, the Sahara may become wetter.
- Monsoon regions may receive rain in fewer, more intense events, harming people and agriculture.
Water-cycle changes also worsen other hazards:
- Drought can increase wildfire risk.
- Alternating wet and dry periods can grow vegetation and then dry it into wildfire fuel.
- Humidity makes heat waves more dangerous because it prevents sweat from cooling the body effectively.
Adaptation is necessary, but reducing fossil-fuel use and climate pollution is also essential to limit future worsening.
Community discussion and engagement are presented as meaningful individual responses to climate change.

Important ideas from Segment 1 not included in Segment 2 (my own notes)

Examples and evidence of current extremes
- California reservoir lows, Fort Lauderdale flooding, and Brooklyn’s extreme rainfall.
- The claim that the most destructive floods now occur about three times as often as before.
The distinction between average conditions and extremes
- The largest storms are becoming stronger even if the average storm is not necessarily changing as much.
- The primary concern is extreme downpours, not merely average rainfall.
Detailed flooding and infrastructure discussion
- Impervious surfaces such as roofs, roads, sidewalks, and parking lots worsen runoff.
- Inland communities face stormwater-flooding risks even when they are far from rivers or coasts.
- Specific adaptation measures: rain gardens, trees, green space, early-warning systems, emergency planning, recovery resources, and public transportation.
New England’s regional climate risks
- Rapid warming of nearby ocean waters.
- Greater land–ocean temperature contrast in winter that can help fuel nor’easters, blizzards, and hurricane-force winds.
Regional variation and uncertainty
- Climate impacts do not follow identical rules everywhere because continents and local wind systems complicate global patterns.
- The Sahara may become wetter despite the broader tendency for many subtropical dry regions to dry further.
- Monsoon rainfall may become more variable and concentrated into fewer, more intense events.
Wildfire impacts
- Drier conditions make vegetation easier to ignite and allow fires to spread.
- Wet-to-dry cycles can build vegetation and then convert it into highly flammable fuel.
Humidity and heat-wave danger
- Humid air makes sweating less effective, making humid heat more dangerous than dry heat.
Mitigation and social response
- The need to transition away from fossil fuels and reduce climate pollution.
- The message that some worsening is already expected, but further escalation can still be limited.
- Encouragement for people to discuss climate change within their communities to build understanding and counter despair.

Karpathy Plugin for Obsidian

I have spent a significant amount of time over the past week or so developing a Karpathy wiki based on a large portion of my Obsidian notes. This process began in April when I decided to purchase a Macintosh Mini I intended to devote exclusively to the exploration of AI on the desktop. I was a bit slow in making this purchase and it took until last week to receive my purchase. My tardiness cost several hundred dollars more than it would have a few months ago.

I was motivated to invest and explore this area for two reasons. First, my main interest in AI continues to focus on retrieval-augemented generation (RAG) of the notes and highlights I have collected to serve as a foundation for my writing projects. As I have used AI plugins to interact with the content I have stored and organized in Obsidian, I discovered that the API-based services for interacting with these notes are relatively expensive because the process of first feeding the notes to the AI service must be repeated each time a session is initiated. Karpathy proposed that AI could be used to create a wiki based on the concepts and connections in a collection of source material, either once (when the collection was created) or as each new item of content was added, and this wiki could then be the focus of future explorations, reducing the cost due to the repeated input of the same content to the AI service.

My second motivator was personal curiosity, sparked by the many posts promoting the potential of AI tools and models that could run on personal hardware, avoiding the costs and scrutiny associated with using online services from major AI companies. The proposal was that many common uses of AI no longer required access to $20 or $200-a-month subscription services.

I understand that another tutorial or “how I did it” post may be required at this point, but I read a post explaining that getting started with self-hosting LLMs will not be easy as the posts newbies are likely to read make it sound, and a good deal of exploration and personalization will be required. The message was intended not to be discouraging but to communicate that “don’t give up, you should be able to get it to work.” This was pretty much my experience, and I thought it worthwhile to explain the issues I encountered and why I had to make adjustments to my specific situation. My experiences with tech since the mid 1980s have kind of gone this way.

So, I have two Mac Minis now, and the first challenge was how to connect both to the same large monitor so I can switch back and forth as required by a general-use and a specific-use computer arrangement. I knew I would have to purchase a KVM (keyboard, video, mouse), but I had not considered that my current setup uses a Bluetooth mouse and keyboard. More specifically, Apple’s Magic Keyboard and Mouse are not intended to be linked to more than one device. You charge your Magic keyboard with a USB cable, so the cable can be used as it has long been used to connect to a computer. You also charge your Magic Mouse, but the cable is inserted on the bottom of the mouse, preventing it from being used while it is being charged. Solution – purchase a mouse with a cable. The first challenge is overcome.

My plan was to use the Obsidian Karpathy LLM wiki plugin because this seemed the most efficient way to create a working system. The plugin’s setup allows selecting multiple AI sources, including subscription services. I did use Anthropic’s Claude API when I was having difficulty getting either of the two local options (Ollama or LMStudio) to work. Claude worked great, but adding one new source document cost 70 cents. My present collection is close to 300 note files, and the work the AI does increases as the complexity of the wiki increases so I treated the success as a sign the struggles I was experiencing could eventually be overcome.

When using Ollama, I was experiencing a consistent problem with some, but not all of the note files the AI was ingesting to build the wiki. I spent a considerable amount of time over several days comparing the files that could and could not be processed and I never did find a difference. It wasn’t the length, the presence of specific markdown tags, the tool I had used to create the original markdown file, or any other variable I could imagine. Nothing. However, the problem was consistent. The same files, time after time, would either work or fail.

My typical strategy in such situations is to ask questions of the Internet. One proposal was that the JSON history had become corrupted. The solution was to reveal the invisible files (the .files and folders) and delete these files. New files would be generated when the Obsidian app was next launched. This was done without consequence.

One issue I encountered was that the models displayed as options within Ollama did not contain the model (qwen2.5) I had found recommended when I read the descriptions of others. I searched how to add other models to Ollama and found it could be done with a terminal command (ollama pull <model name>. Now qwen2.5 appeared. Qwen3.6 was originally listed and I assumed there would be little difference, but for some reason, I was wrong, and the system worked with qwen2.5.

Without going into details because others have already provided tutorials, you first add and install the Karpathy LLM Wiki plugin for Obsidian. The gear icon associated with this community plugin provides a “fill in the blank” form where you enter information linking Obsidian to the AI online service or local option you want to use.

The wiki construction process is controlled by the commands that appear in the Obsidian command list when the Karpathy plugin is installed.

So, you start Ollama and select the model you will use in Obsidian. Start Obsidian and select the command to ingest a file or folder and be patient. Eventually, your wiki will be generated, and you can query the wiki rather than the source files. The right-hand column displays a response to a prompt.

So, I was able to generate a wiki based on more than 150 of my notes. In examining some of the components of the wiki I did find some weird artifacts. There were some with Chinese characters. I happen to be listening to LeoLaporte talking about different AI models and he said that qwen originated in China. It then made sense to me that the model might translate some of the Chinese names in my article summaries and include their Chinese translations (no idea if that is actually what happened). I also found some md pages with titles, but no content.

When I used the Karpathy command to submit queries, I found the quality of the responses to vary. Some made sense and some ignored sources I knew existed and were central to what I expected. Rewording of the query in ways I thought the model should have understood as equivalent sometimes resulted in the response I expected.

The following comparison (first from querying my new wiki, and the second from using Claude with my original source files) demonstrates fairly substantial differences. As you can tell from a quick skim, there are significant differences in the eloquence and completeness of the responses. The sample responses are presented as an Appendix.

Summary

Each user is likely to have a unique experience when applying a local AI system to their content to generate a Karpathy-style wiki. Expecting that it will take some tinkering is good advice even when following instructions or “how to do it” videos on YouTube. My effort here was to describe what this tinkering experience looked like.

After at least getting the system to work, I was able to compare my first successful effort with what I generated for 44 cents using Claude. The differences were substantial.

I assume there are many more issues to explore. I am currently repeating the process with a different script that instructs the AI on what to do when creating the wiki. If that effort produces substantial improvements, I will have to write another post.

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Response of local model to wiki

Summary of Research Comparing Handwritten Notes vs. Computer Notes

According to the literature summarized from various studies, there are several key findings regarding the comparison between handwritten notes and notes taken on computers:

Repetition in Note-Taking:

– A study by Bui et al. 2015 suggests that repeated exposure to material enhances learning outcomes, especially when coupled with effective note-taking strategies.

Handwritten Notes and Memory Retention:

– Longhand notes are described as more paraphrased and containing more signals and images, which can lead to higher text-related achievement when reviewed (from Longhand Notes).

– Lecture Note-Taking Strategies indicate that students who take notes by hand outperform those who use digital devices, as handwritten notes require a higher level of processing and engagement.

Editing and Revisions:

– Research by Mueller and Oppenheimer (2014) found that while typing is faster, it leads to less cognitive effort and thus less retention due to the lack of engagement required in handwriting notes.

– Follow-up revisions are crucial as they enhance memory retention. According to a study cited by AI response, revising digital notes can mitigate some of the disadvantages of digital note-taking, improving long-term recall and academic performance (from AI Response to Challenging Luo Study).

Professional Note-Taking:

– Additionally, in the context of professional training, effective note-taking is vital for future practitioners (from “Notetaking in Professions”).

References

Longhand Notes — Core mechanism explaining why handwritten notes are more beneficial.
Bui et al. 2015 — Effects of Repetition on Note-Taking and Recall Strategies in College Lectures.
“Notetaking in Professions”. Stacy and Cain, American Journal of Pharmaceutical Education (2015) — Importance of note-taking skills for professional training.
Mueller and Oppenheimer (2014) — Pen and Pad versus Laptop for Note-Taking.
AI Response to Challenging Luo Study — How Note Modification Research Might Counter Luo et al. (2018)

*******

Paste from Claude plugin within Obsidian

Handwritten vs. Computer Note-Taking: What Your Notes Say

Your vault contains extensive research on this topic, and the picture that emerges is more nuanced and contested than the popular narrative suggests. Here is a synthesis of what your notes reveal:

The Foundational Study

The most influential study is Mueller & Oppenheimer (2014), documented in Pen is mightier than the keyboard. Their key finding was that faster keyboard transcription led to more verbatim, less generative notes, while the slower speed of handwriting forced summarization and paraphrasing. Notably, their Study 3 found no differences when note review was not allowed, but longhand was superior when review was allowed—a surprising result.

The Generative Processing Argument

Several of your notes outline the theoretical rationale:

Translation hypothesis: Writing adds a distinctive encoding channel beyond just listening (Luo, Linlin, Kenneth A. Kiewra, Abraham E. Flanigan, and Markeya S. Peteranetz2018)
Generative hypothesis: Handwriting’s slower speed forces paraphrasing, organization, and integration with prior knowledge, leading to deeper processing (Depth and Generative, EncodingEffect)
As noted in transcription speeds, adults type at ~33 wpm and write longhand at ~22 wpm, meaning handwriters must be more selective

Your note in transcription speeds raises a sharp critique of this logic: it “suggests a strange lack of awareness on the part of the learner. It would seem that the keyboarding learner is oblivious to the benefits of generative processing and goes with what is easier, but less effective.”

Contradictory and Inconsistent Findings

Your notes consistently emphasize that research findings in this area are contradictory:

Urry.et.al documents a major replication of Mueller & Oppenheimer that found inconsistent results. Urry found laptop users took more notes and more notes related to better performance. Critically, they differentiated quantity from verbatim overlap—quantity was positively related to performance while verbatim proportion was negatively correlated.
Luo, et al 2018 found that when notes were recorded but not reviewed (process function), laptop was actually better for image-related learning. Only when notes were reviewed (product function) did longhand show an advantage. The authors themselves note that “findings in this area of research are often contradictory and uncontrolled variables may be responsible.”
Flaniganetal2023 found that, surprisingly, longhand and computer note takers recorded a similar number of ideas during the lesson, contradicting the expected computer advantage. However, longhand revisers added three times as many complete ideas during revision pauses.

The Note Completeness Counter-Argument

Your notes in NoteCompleteness and Colllaborative Notes document an important counter-narrative: note completeness predicts achievement, and computers produce more complete notes. As noted:

“The studies of note volume and completeness would seem to challenge the argument that taking notes on a computer is a liability because more notes are taken implying less thinking about the content.”

Bui et al. (2013) found that students with access to their more complete (computer-generated) notes demonstrated better performance, directly contradicting Mueller & Oppenheimer’s interpretation.

The Revision Factor

Multiple notes highlight that the handwriting vs. computer debate may miss the most important stage entirely—revision:

Three stage model of note-taking proposes that effective note-taking is a three-stage process: recording ? revision ? review, and most studies ignore the revision stage.
Cojeanatal2024 found that computer note-takers were more likely to reformat and revise their notes after class, potentially compensating for less generative initial encoding.
Flaniganetal2023 found longhand revisers added more ideas during revision pauses, but this study used interspersed pauses—an artificial design unlikely to reflect real practice.
Cohen2013ActiveSummary demonstrated that note restructuring significantly improved exam scores regardless of medium, suggesting what you do after taking notes matters more than how you take them.

The Children’s Research

Your note childrennotes documents a unique study (Horbury & Edmonds, 2021) with 10–11-year-olds that found:

Conceptual understanding was superior with handwritten notes
Unlike adult studies, there were no significant differences in note quantity between conditions
The authors speculated that children’s limited keyboarding experience may have increased cognitive load when typing, a different mechanism than proposed for adults

Your AI-Generated Synthesis

Your note AI response to my question challenging the Luo study that assumed the notes themselves could not result in generative activity offers a compelling reframing:

Luo et al. Conclusion	Modification Research Counter
Laptop notes are transcription-oriented	Generative processing can be added during revision
Longhand notes are better review products	Laptop notes can be transformed into superior products through revision
Laptop notes lack images and signals	These can be added easily using laptop affordances
Reviewing longhand notes yields higher achievement	Studies only tested passive review, not active modification
Longhand is generally preferable	The medium matters less than the strategy

Overall Conclusions From Your Notes

The popular narrative favoring handwriting is overstated. Replication attempts and meta-analyses show inconsistent results.
The mechanism matters: Quantity of notes is positively related to achievement; verbatim overlap is negatively related. These are separate variables that should not be conflated.
Most studies use artificial conditions (immediate testing, brief delays, no real revision opportunities) that don’t reflect actual academic practice.
Revision and post-processing may be more important than the initial recording medium, and computers offer superior affordances for revision.
The real issue is strategy, not medium. Untrained students default to transcription on keyboards and forced summarization by hand—but training could equalize outcomes.
Note completeness matters for the storage function, and the storage function is generally more important than the encoding function (ExternalStorage).

The U.S. and China AI Competition

The very recent summit involving Presidents Trump and XI Jinping dealt with many political controversies of the day, which included AI and related issues such as intellectual property. The mention of AI brought to mind a book by Kai-Fu Lee, which I think I read in 2019. I remembered some of the comments Lee made about China, computer science, and AI at that time. Lee, who has held both U.S. and Taiwanese citizenship, wrote that China would have important advantages in the development and application of technology, which surprised me at the time but made some sense given what I knew about China. Lee was educated in the U.S. (Carnegie Mellon Ph.D.), worked for Apple, then returned to Taiwan and later worked for Google in China. I explored my notes and highlights from that book and also from The Big Nine. My interest in the role of AI in education and its application across different countries led me to another article in my personal archive (Hao, 2019). The following comments are mostly based on Lee’s ideas, with some expansion using the other two references I have mentioned. All sources are a bit dated, given the rapid pace of AI developments, but I still find the core ideas worth considering.

According to Lee, China’s advantages in AI come from scale, data, industrial capacity, talent, and state coordination.

Scale equals more data

China’s 1.4 billion people give it control of “the largest, and possibly most important, natural resource in the era of AI: human data”—and that its huge number of internet users gives it both data quantity and quality for training models. This resource is roughly the equivalent of the combined resources of the United States and Europe. Lee offered this perspective some years ago when finding content seemed more a priority for U.S. companies who encountered push back when scrapping the web and books without permission.

Industry integration

Chinese companies share. For example, Tencent’s ecosystem is noted as perhaps the single richest data ecosystem of all the giants and combines multiple services, say, in contrast to X and Amazon. Concentration of data and services in a few massive platforms offers a related quantity and quality advantage.

Quantity of Talent

There is a Thomas Friedman quote I have always remembered. “Remember in China if you are a one in a million talent, there are 1400 others just like you.” Lee offers a different assessment of the talent situation specific to AI. He claims that the U.S. has more superstars, but China has the advantage in the number of engineers and computer scientists working in on AI and related fields. Aside of the great difference in population, engineering, programming and science are simply fields of advanced study that are seen as more of an opportunity in China. My own way of thinking about this difference is that in the U.S., business and finance attract many and in China these fields are less of a draw.

State Coordination and Standards

A “big advantage for China: it doesn’t have the privacy and security restrictions that might hinder progress in the United States”. The commitment to the massive surveillance of its own population is known focus of the Chinese government and a means of control and manipulation of its population. We rightfully consider the use of technology to probe the personal lives and values a violation of basic human rights and bristle internally at the collection of information about us by companies and the government. Simply put, China doesn’t have the privacy and security restrictions that might hinder progress in the United States. Despite tolerated abuses, the commitment to collecting and analyzing this type of information is a source of funding and a focus of experimentation in China.

” Move fast and break things” was the original Google creed, but a value system that has come under increasing criticism in China. Without the pressure to curb potential negative aspects of AI, China moves faster. Related to this is the greater top down decision making of the Chinese system. In the U.S., you have multiple businesses trying to raise huge sums of money and are often isolated from each other, often duplicating similar approaches. We historically value competition and assume the motivation has advantages. While true, I wonder about the “business model” sucking up a large share of the available investment money in this sector in the US. The amount of money required has to a great degree squeezed out university researchers who either leave universities or work around the edges of AI innovation. While AI research is a high priority in China, the U.S. has cut funding for NSF funding for AI and cybersecurity.

AI in China and Education

The personal interest that has driven my own interest in AI has been potential opportunities in education. This has been a messy issue in this country with pushback due to legitimate concerns for cheating, failure to address skill development, and lack of interest in instruction presented by a computer. China has committed to exploring AI-facilitated education.

Academic competition in China is tense. Millions of students a year take the college entrance exam, the gaokao. Your score determines whether and where you can study for a degree, and it’s seen as the biggest determinant of success for the rest of your life. Parents willingly pay for tutoring or anything else that helps their children get ahead. The options tech can provide outside of classrooms offer opportunities to sell experiences to well-meaning parents. (Hao)

Two companies that are likely unfamiliar to most U.S. educators, Squirrel AI and Alo7, make good example. Since the Hao article was published both services became available in the U.S.

Squirrel AI uses an “adaptive learning” model that breaks subjects into thousands of “knowledge points”—far more granular than traditional textbooks. The system diagnoses a student’s specific gaps and provides targeted video lectures and practice problems. The teachers are intended to act like “pilots,” stepping in only for emotional support or complex issues while the algorithm handles the core instruction. Educators will likely recognize similarities to the Kahn Academy.

In contrast, Alo7 emphasizes a “quality-oriented education” focusing on creativity and the liberal arts. This “intelligent classroom” use AI to analyze student engagement, pronunciation, and even “joy” through facial and vocal recognition

The interest in AI in education seems to be a combination of the emphasis of standardized test performance for advancement and opportunity, the larger population, and the greater risk tolerance within the context of exploration for improvement.

Summary

This post is not a value judgment comparing U.S. AI policies, but rather an attempt to summarize what some experts have said about the differences. My personal issue concerns the economic pressure in the U.S. based in our trust in competition among corporations to drive innovation. While this is an approach that has worked in many areas, the huge investments that are required have to this point sucked a great deal of capital from the economy and seem largely and unnecessarily redundant. I personally also find the focus of interest in AI in education (personalized and adaptive instruction) interesting as this emphasis has appealed to me based on my interest in mastery learning.

Sources

Hao, K. (2019). China has started a grand experiment in AI education. It could reshape how the world learns. MIT Technology Review, 123(1), 1-9.

Lee, Kai Fu. 2018). AI Superpowers: China, Silicon Valley, and the New World Order. Boston, Mass: Houghton Mifflin.

Webb, A. (2019). The big nine: How the tech titans and their thinking machines could warp humanity. PublicAffairs.

Hallucinated Citations and Related Problems

Many of my posts are based on applying research to personal or classroom practice. I am retired, so I am no longer involved in experiments myself, but I now spend time reading both new and older published studies on a topic that interests me.

My change in location and social circles has led to some adjustments. I can’t walk across the street to a university library, though I still have access to online resources. Without students and colleagues, my interests are now far more self-driven and self-perpetuated. I have used Google Scholar since it was around, but the emergence of newer AI-supported tools for investigating the literature has been of great personal value.

This shift in how I locate the articles I read has exposed me to a strange phenomenon. I get excited when I find a reference relevant to a topic I have missed, particularly when it comes from an influential, productive researcher I follow. The title of this discovery sounds perfect and seems to promise just the type of evidence I have been looking for. I access my library’s online resources, call up the appropriate journal, and enter the title from the citation. The article isn’t there. Maybe the volume or the year of publication isn’t correct. I enter the title in Google Scholar to do a search and related articles appear, but there is no match for the specific paper I want. The citation that generated my excitement is very likely an AI hallucination.

I first wrote about this issue several years ago when AI was itself less sophisticated and this problem was probably more common. I include the link to this previous post because it contains multiple examples of what such hallucinations look like. I decided to revisit the topic after reading a recent Nature article examining this issue. The recent article did a good job of explaining why such hallucinations seem so real, but also raised questions related to how such hallucinations could appear in newly published research and how and why scholars might end up citing and developing arguments in their own papers related to some literature that does not exist.

The structure of a citation and why it results in hallucinations

The Nature study included a visual representation of a citation that I found helpful. I did not want to just cut and paste their examples so I had an AI tool develop something similar.

Think of a citation as consisting of several elements and understand that AI is not itself cutting and pasting what it offers in response to a prompt, but generates content. When this happens, some of the possibilities can result in fake outcomes.

Author may have published in this general area
Authors may have published together but not this paper
Words in the title are consistent with some of the work the author has done so are used to create the title
Pages fit with the date for this journal but are not appropriate
DOI (digital object identifier) – does not point to anything, but is similar to other DOIs for this journal

Ironically, trying to have an AI tool generate a plausible citation and identify its components also resulted in hallucinations (compare the image below with the one above). I tried multiple iterations to get what I wanted, but finally, I just had the tool generate the figure without lines, then used a different app to manually add them myself.

What are the responsibilities of an author?

How hallucinated citations appear in published work raises other serious issues. Possibly, the author who submitted the paper used a tool to build the reference list, but did not then check the final product. More seriously, the author used AI to write sections of a paper complete with citations and did not actually read the original papers.

Check your references

In my own efforts to explore relevant courses of action, I learned that many publications now rely on services that verify citation authenticity. I checked on the services and did not find anything that would be financially feasible for individuals. I did find that there are tools, some free, that will check a reference list.

CiteTrue

CiteTrue is a free online tool that accepts a list of citations and checks each component for accuracy. The following image shows what this looks like. I used part of the list of hallucinated citations I included in the previous post on this topic I describe above, and pasted these into the input box. The output indicated all were inaccurate and speculated about what was incorrect.

Personal Comment

This is not an issue I personally worry about, as I am no longer an active researcher. I do cite sources in some of my posts when a reader cannot follow a link to the source. I admit that not all of my sources follow the APA (American Psychological Association) format. This is due to my laziness. I do read all of the papers I cite, but putting together a citation is sometimes a manual process of accurately pulling together different pieces of information from the pdf for that source. I often copy the title from the pdf and paste it into Google Scholar and then use the citation for that source provided by Google. I am unclear how Google assembles citations in its systems, but they do not always follow the most recent APA guidelines. For example, many do not include a DOI or list the authors in different ways. I know the titles work because that is how I find the citations.

Smart Connections finds note connections

Smart Connections discovers and reveals related notes in Obsidian. I started using the Obsidian plugin Smart Connections because I wanted a way to apply AI interrogation of my own notes. I wanted to request cross-note summarizations and generate a variety of sample written products (e.g., blog posts) based on my personal notes and highlights. I ignored an important capability that is claimed based on the product’s name — the identification of note connections.

Without an AI-based method for identifying possible connections among notes, Obsidian relies on the user to establish connections via links and tags. I was aware that other services (e.g., Mem.ai) suggested that a note retention system could do better and offered tags and links, but also made the claim that AI would help surface connections. Some would argue that exploring your Obsidian content repeatedly and finding connections are important parts of the process of personal knowledge management. Constantly working with your notes is an active cognitive activity that encourages connections between what is internally retrievable at a point in time and what you are accessing in Obsidian. New connections first brain to second brain and within Obsidian may emerge. This constant interactive process is suggested by what I would describe as the Zettelkasten practitioners. I don’t think this advice must be rejected for users who want to use AI to surface new connections.

Smart Connections makes use of AI, and the AI creates a numerical representation of the content of each note and stores these as what are called embeddings. You must subscribe to an AI provider via an API, which is far less expensive than a subscription to such a service. You have the option of basing such representations on blocks within notes rather than entire notes. I make use of this option because I store lengthy notes containing book and pdf highlights, such that a representation of an entire note does not represent a level of detail that is very useful for finding something useful in such lengthy notes. In the content that follows, I will show where to turn on block embedding.

Smart Connections works by requesting connections for a note that you have selected. The following image shows Obsidian with Smart Connections active. The green rectangle in the menu bar is used to activate the Connections as opposed to the Chat capability of Smart Connections. The up/down symbol allows you to scroll through the associated notes/blocks from most related to less related. The gear symbol is used to access settings for Smart Connections. The middle panel is the active note, and the right-hand column represents a hierarchy of related notes/blocks.

Getting back to how I think AI may supplement the more hands-on use of Obsidian, I would recommend that in examining connections to a given note that you then use tags or links if you want to create permanent connections.

The extension of Smart Connects from note to note to note to block is worth doing if you do not keep atomic notes. Start with the Gear icon (see image above). This will reveal multiple setting options. What you are searching for are the environment settings. Open these settings with the button shown below.

Once more settings have been revealed, you are looking for Smart Blocks (see below). You turn this option on and specify a minimal length. I did not keep a careful record of the source for advice I followed and I apologize to the author, but I entered 300 characters, and that seems to work well. There are many other settings and I have mostly stayed with the defaults.

Summary:

Smart Connections is an Obsidian plugin (free) that allows AI capabilities to be applied to the notes stored in Obsidian. Chats allows a user to generate AI prompts that are applied to the contents of Obsidian. Connections generates a list of notes (note blocks in the setup I have described) associated with a selected note and is helpful in the identification of such relationships in a large collections of notes.

Considering AI in Writing and Reading

This is a personal exploration of what I think about the role of AI in writing and reading. Once you begin exploring these topics I think you discover how nuanced they are. I do understand outlets for written content are being pressured or have decided on their own to take positions on what is allowed. I will offer a suggestion at a later point.

Writing

We all make observations based on personal experience. I am a writer and as an academic wrote research papers and a couple of textbooks. This writing was before AI and there were strict rules of personal accountability that applied that were severe enough that your career would be at stake if these expectations were violated.

As an educational psychologist I followed the literature on learning to write and the benefits of writing to learn. Writing is a procedural skill and as such requires the use of the skill to develop proficiency. I believe that this proficiency transfers to speech so there is no way I can imagine of developing important communication skills without spending time using the skill. In academic situations writing is a more efficient group activity than individual presentations so time must be invested. Writing to learn seems an efficient way to develop writing skills and has unique benefits as a way to process all experiences. Many of my posts focus on generative activities – external tasks that encourage productive cognitive skills – and writing makes a great example. Organization, integration, personalization all are required in writing and in understanding and application. Again, writing assignments are an efficient way of encouraging personal cognitive activity within a group setting.

These personal benefits aside how important is it that I write without assistance. “Without assistance” is key here as I can simply provide a prompt to an AI tool to create a product based on fairly basic expectations. This is one extreme of the AI in writing continuum. At the other end are spelling checking and the types of structural improvements I can apply with the assistance of Grammarly. In the middle are various strategies I might use to request AI to offer suggestions for topics and broad organizational ideas I might then implement myself. Closer to the “write it for me end” are requests for a product I might then paraphrase. My guess is that the line of acceptability is drawn somewhere within this continuum and will likely shift over time.

Reading

The basic question I am asking here is does it matter that the content I read was written unaided by a person? First, I should acknowledge that while I read a lot, I seldom read fiction. I seldom read content that depends on the creativity of the writer. I understand that is reasonable to recognize the beauty or creativity in much the same way different musicians can express the same underlying composition. I seldom focus on such skill in the authors I read. I want to understand why things are as they are whether it be history, science, economics, or politics. If facts are available, I want to know the facts. If opinion and logic are all we have, I want to understand the logic behind the valued opinions. Good writing is important in my ability to understand and process this content, but I am not certain that the human touch is required for a quality product.

Clearly, what AI is doing is summarizing massive amounts of content that was generated by humans and I do think that this source material is essential, but I am not certain that the translation process requires a human writer. Human writers will continue to be necessary to make the observations and run the experiments and explain what they have observed, but this type of communication is several steps down the food chain from my consumptions.

Why post this here?

I think outlets are making decisions about what role AI will be allowed to play. I think some outlets will opt for human writing only and I think this is fine if the goal is to focus on human unaided creativity.

I think there is a middle ground that has just recently occurred to me. I write based on a tradition that requires careful source citation. I still do that in some of posts here when what I write had its origin in a book or journal article. Why not indicate when a segment of what is posted was written by AI?

Out of curiosity I asked Perplexity if there were conventions for citing AI as a source. It turns out there are formal ways to cite AI content. I think there are ways to make AI content more obvious. One might indicate it as quotations that appears in an obvious way such as the italicized and indented style used in WordPress. Or, one could do something like the following. This is what Perplexity had to say about AI sourcing.

————————————————————————————————

To attribute AI-generated text in your writing, follow these guidelines:

1. **Explicit Mention**: Clearly state that AI assistance was used. For example, “This section was generated with the help of OpenAI’s ChatGPT” or “Drafted in collaboration with ChatGPT”[2].

2. **Citation Styles**:

– **MLA**: Format as: *”Description of chat” prompt. Name of AI tool, version, company, date, URL*. Example: “Explain how to bake a cake” prompt. *ChatGPT, Mar. 2025 version, OpenAI, 2 Mar. 2025, chat.openai.com/chat*[1][2].

– **APA**: Include the tool name, date, and URL. Example: *OpenAI. (2025). ChatGPT (Mar 2025 version) [Large language model]. https://chat.openai.com/chat*[2].

– **Chicago**: Example: *Text generated by ChatGPT, OpenAI, March 2025, https://chat.openai.com/chat*[2].

3. **Placement**: Place attribution prominently in an introduction, acknowledgment personal section, or footnote to ensure transparency[2].

4. **Accurate Language**: Use terms like “assisted by,” “enhanced with,” or “generated using” to describe the AI’s role without overstating its contribution[2].

This approach ensures ethical transparency while crediting AI appropriately.

Sources

[1] Citation and Attribution – Generative Artificial Intelligence https://libguides.brown.edu/c.php?g=1338928&p=9868287

[2] Demystifying Attribution: Giving AI Credit – LinkedIn https://www.linkedin.com/pulse/demystifying-attribution-giving-ai-credit-donna

[3] Used a AI-Detector on my own text that I have writen … – Reddit https://www.reddit.com/r/ChatGPT/comments/14cls37/used_a_aidetector_on_my_own_text_that_i_have/

[4] Using the AI Autofill Feature for Text Attributes – Help Center https://help.plytix.com/en/ai-autofill

[5] A Comparison of Several AI Techniques for Authorship Attribution on … https://www.mdpi.com/2227-7390/10/23/4589

[6] Is there any tool which can detect AI writing. Because ChatGPT itself … https://www.reddit.com/r/SEO/comments/13s5zb0/is_there_any_tool_which_can_detect_ai_writing/

[7] How To Identify AI-Generated Text? – Originality.ai https://originality.ai/blog/identify-ai-generated-text

[8] Measuring Attribution in Natural Language Generation Models https://direct.mit.edu/coli/article/49/4/777/116438/Measuring-Attribution-in-Natural-Language

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Summary

These are my personal thoughts about AI in writing and reading. I don’t see AI going away so I believe it is important to work out what is appropriate and what not. These are nuanced decisions, but a rationale for individual decisions should be stated. I propose that attribution is important when using AI to be read by others and provide and example of how this might be handled.