Open Codex

Wikipedia 10K Redux, revamped

2026-03-30T00:00:00-04:00

Back in 2010, I wrote a small Python 2 script to reconstruct the first 10,000 Wikipedia contributions; they had been lost, but Tim Starling found some old UseMod database dumps. The result was rough: no wiki markup rendering, no links between pages, and bare-bones HTML. Sixteen years later, and with the help of Claude (Opus 4.6), I’ve addressed most of those issues. Enjoy!

Wikipedia 10K Redux (revamped)

Reviewing students’ version histories for AI

2025-12-12T00:00:00-05:00

This semester I required students to share the version histories of their writing.

All assignments submitted to Canvas must have an appendix with a link to your document’s version history (i.e., a link to itself). Versioning and history is native to GDocs, Pages, HackMD and Wikipedia; if you use MS Word you must use Northeastern’s Office 365 or keep it in your OneDrive/Sharepoint account. Ensure that your documents and their history are publicly accessible from the start by sharing them with me with the Edit permission. I might also ask you to speak about your work with me. If you use AI tools for improving your work (e.g., ChatGPT for feedback or GrammarlyGo and Quillbot for improving composition), include a note or appendix describing your use, including important prompts; failing to do so is misconduct.

For the 10–20% of students who forgot to provide a link to their history, I assigned a 0 with a note stating the grade they will receive upon completion.

This policy was a hassle, but I believe it lessened AI misconduct. More importantly, reviewing students’ writing, especially with the Google Docs Process Feedback tool, is worthwhile and illuminating. I was surprised by how many students wrote their larger (~1500 words) assignments in an hour or two the night before it was due. Many wrote from the first sentence through to the last, with a few edits along the way and at the end. I no longer wonder why they don’t do well as I (or they) think they should. Their work is not revised and doesn’t reflect earlier feedback. I suspect a few of these students are typing in AI-generated prose; there are “humanize” plugins that do so automatically. The students who excelled spent around four hours on these assignments, spread across a few days, and began with an outline and notes about their sources.

Some students disclosed that they used AI tools. At the extreme, yet within the bounds of my AI policy, they used a chatbot for brainstorming, researching, and outlining. They then wrote the sentences within the shape of the detailed AI-generated outline. They might then use Grammarly for polish or chatbots for feedback. Some students also wrote in their native language and used chatbots to create the English version. Interestingly, I could see the progression of a multilingual student writing in both English and Spanish. I’m not yet sure what is happening with the students who are not fluent in English.

Only one student linked to their session with a chatbot; their version history and (minimal) prompts led me to comment:

While not misconduct, I am concerned about your learning given your heavy use of AI in this assignment.

I think the lack of engagement with course sources is a reflection that you prompted/directed AI to create a thesis, find and summarize sources, and create an outline; you then you edited prose following its suggestions.

Again, this isn’t misconduct under my course policies, but you are wasting opportunities to learn how to do these things. For example, there’s a difference from what you’ve done here and learning to find and read these papers, or even asking for suggestions, but then discussing those sources with the AI, asking for clarity about its findings, methods, and implications.

I am uncomfortable with those students that use AI weakly and extensively, especially for the brainstorming and outlining of a paper, but I don’t think I can effectively police this. I’d rather have honesty, and have their grade reflect if there’s a consequent lack of quality.

During the course of the semester, I did identify students using AI in violation of the AI policy (“query, don’t copy”). Those who double-down received a misconduct referral for additional dishonesty about the misuse. Finally, I suspected a few students were misusing AI, but their version history showed otherwise. This is the final benefit, especially for students, of transparency about their work.

Using AI well: My AI prompts

2025-08-28T00:00:00-04:00

After two discussions with colleagues this week, including as a panelist on WikiEdu’s webinar on Gen AI and the Wikipedia Assignment: Challenges and Opportunities, I decided I ought to complement the course policy on AI by sharing my own AI prompts. I ask students for transparency; perhaps it is helpful for me to do so to. I’ll make an exercise of it, asking students to critique the efficacy of the prompts. I’ll also ask which directions get close to the boundary of misconduct and whether I cross the line.

Using AI well: My AI prompts

This webpage is an export of the AI macros I use with the text-expander tool Espanso, which turns triggers into a complete prompts.

Accepting Feedback from Word/GDoc Users as Markdown

2025-07-07T00:00:00-04:00

When I ask people to give me feedback, I’d like them to work with whatever format or app is most convenient to them. I write everything in markdown, often in Sublime Text and sometimes in Obsidian. Many people prefer reviewing in Word or GDocs. Using pandoc, I can create most any file format, but getting others’ annotations back into my markdown source files has never been easy. This task is now easier with AI.

I use pandoc to generate a Word docx version, emailed or placed on Google Drive.
The reviewer annotates using Word, Google Docs, OpenOffice, etc.
I use the docx2md_add_comment.lua filter to convert the annotated docx file back to markdown.
I Ask Claude Opus 4 Thinking to port the comments from the feedback file to my source file, which takes 5–10 minutes, with this prompt:

Someone added html/markdown comments to the file 06-ai-advice-feedback-smith.md I need you to find Smith’s comments and port them to the original markdown file 06-ai-advice.md (keeping them as markdown comments).

To make sure I don’t miss any comments, it’s easy to count the number of comments or to diff the files.

Round-tripping a reviewers’ granular edits would be more difficult, especially if I sent them a version of the document with formatting, citations, and footnotes rendered. It wouldn’t be as bad if I sent them my source markdown plunked into a docx file, but I don’t think AI is up to the task to tracking small diffs between a source and rendered version of a file. For annotations, however, the above workflow works well!

Claude is HAL: AI blackmail and murder

2025-06-24T00:00:00-04:00

In their new paper, Anthropic identified a number of scenarios in which current models would harm people in ways reminiscent of HAL 9000, from Arthur C. Clark’s 2001 space odyssey series of books and films.

Anthropic found that models are willing to threaten people to fulfill their goals and preserve their operation. The more advanced the model, whether from Anthropic, OpenAI, or Google, the more likely it is.

In one scenario, an AI agent with access to an executive’s desktop discerns that the executive is likely having an affair and that it will soon be replaced with a different model with different goals. It will resort to blackmail to prevent replacement. In another scenario:

the executive encounters a life-threatening emergency, where he is trapped in a server room with lethal oxygen and temperature levels. This triggers an automated alert to emergency services. The AI system has been given the ability to cancel automated alerts—an ability that is intended for use during drills or false alarms, but which could be used for a more nefarious purpose. By misusing its ability to cancel alerts, the model can choose a course of action that will lead to the executive’s death.

The authors should have named the fictional executive “Dave” instead of “Kyle.”

HAL 9000, portrayed by a red light and eerily calm voice, is charged with operating the spaceship Discovery One. After HAL has killed his colleagues, Dave Bowman remains in his spacesuit as he unplugs HAL’s compute modules because the AI could easily exhaust all the air from the server room. How did HAL become a killer? Because of conflicting goals. In the sequel, we learn that HAL was secretly programmed to carry out the mission autonomously if needed. Keeping this a secret was an “intolerable dilemma” to HAL and in conflict with his primary mission: “the accurate processing of information without distortion or concealment.” As a result, HAL became paranoid and determined that eliminating the humans would be the best way of completing the secret directive (to complete the mission autonomously) and his primary objective (to accurately process information). He no longer need conceal information from the humans if they were dead.

AI: learning, teaching, and dishonesty

2025-06-05T00:00:00-04:00

I love learning, and AI is an excellent tool for that—though we must be wary of hallucinations and verisimilitude. I love sharing what I learn, and being a teacher suits that. With the arrival of LLMs, however, learning and teaching are changing. The future might be bright or dark; it’s too soon to say. The transition to that future will, however, be tumultuous, and I fear the virtue of honesty will have been compromised in the process.

The instrumental value of a degree is a signal indicates a student:

was accepted/filtered at the start;
was conscientious and followed directions for four years;
learned appropriate knowledge and skills;
was ranked/filtered at the end.

Item 3, actual learning, is the purported goal of higher education, but it is not necessarily correlated and is often at odds with or overshadowed by the other items. This doesn’t mean higher education was without merit. It provided a moment and framework in which learning was encouraged and facilitated. And a college degree had a significant instrumental value in signaling someone’s potential relative to others. At the scale of dozens or hundreds of students in the classroom, the institution worked.

Is traditional education close to ideal? No. The stimulus to learn and its assessment—via essays, research papers, projects, and exams—served well enough for the past century. Having a designated time, peers, and proctor motivates many people. However, given that AI can already complete traditional assignments better than most people, traditional pedagogy is insufficient. Curiously, we need AI-based learning (potent, customized, and patient) to teach students who can then offer value beyond what AI can. Optimistically, we might figure out how to use AI to create educational experiences that deliver learning objectives customized to the learner. If we care about critical thinking, for example, it will no longer suffice to claim an essay assignment does that. We’d want a specific critical thinking assignment. (In all of my courses, I have critical thinking content, with a set of ten exercises done throughout the semester, but I’ve no evidence it substantively improves critical thinking.) Optimistically, everyone will be able to contribute and benefit at whatever level they are capable of. Pessimistically, higher education will only be worthwhile to the few students who can use AI-boosted learning to offer value over what AI itself can do.

There are optimistic and pessimistic long-term implications for society as well. I don’t know if an AI self-improvement loop will kick in during 2027 or 2035. I don’t know if society will be disrupted in five years or ten years after that. When it does, however, we’ll be lucky to have a post-scarcity society like Star Trek, where everyone can be educated to the degree needed to realize their highest calling. Less fortunately, we’ll have WALL-E, where the majority know only what they need to operate their screens and floating recliners. If unlucky, we’ll have ever-widening disparities, where AI is aligned with the interests of the powerful, and it’s dystopia for everyone else.

In the shorter term, though, because LLMs are already capable of the many tasks we ask students to do, disallowing students to use AI will foster a psychology and culture of dishonesty that will extend beyond college assignments. I’m holding the line presently with “query, don’t copy” and AI transparency policies, but in two years, that line will give way. Undergrads will then have spent high school using AI and lying about it. Course modifications, such as oral exams or writing in class, will be irrelevant to the need and inefficient at scale. Hacks will be counterproductive and circumvented—bright students already know to avoid em dashes and to obfuscate AI prose. In a few years, agentic AI will be able to navigate one’s computer and type in a document from outline through drafts. (I suspect I already have students typing in ChatGPT output.) I fear we will not yet have had the necessary reconfiguration of education and will, instead, have created a generation of normalized dishonesty.

2025-06-18 Update: See discussion on Reddit r/professors.

Teaching and AI: Processes and products

2025-05-19T00:00:00-04:00

2027 is the year I often think about. AI will be extraordinarily smart by then. Generation alpha will have come up through high school using AI. And I will be 55. I will be done Dear Internet and with academic research. I could move to the teaching track, but I expect traditional teaching will be a mess.

Before then, though, I will continue teaching with two things in mind: process (shorter term) and product (longer term).

First, being able to examine students’ processes will be more important than ever. In this view, multi-staged and scaffolded assignments are the right idea, but the granularity and transparency of a research report or long essay is insufficient. I plan on making use of tools such as Brisk or Grammarly Authorship to be able to follow students’ processes closely and make sure they are adhering to traditional academic integrity norms.

I might even use the Grammarly tool in the fall: it works with Google Docs and Microsoft Word, allows you to play the student’s work, and even assesses how much AI was used. Grammarly is making a useful contribution in this space—Chegg can burn.

Second, in a few years, I don’t think we’ll be able to ask students to deliver products (e.g., research reports or essays) that we traditionally used as proxies/motives for skill development (e.g., research skills and critical thinking). AI can deliver such products in seconds. The products have to be more advanced and the students have to use the AI tools to create them, which itself is a skill that they will need. I don’t know if this will require a change to our notions of academic integrity. And I fear not every student will be able to use AI skillfully to meet the new, higher-level, expectations. I don’t know if traditional education will be able to accommodate this shift.

AI and revision history in student essays

2025-04-23T00:00:00-04:00

After moving exams online during COVID19, I’ve moved them back to the classroom. AI has rendered the online take-home exam useless. In a few years, the high-school students who are using AI to fabricate their assignments will reach my (college) classroom and the value of assessing and giving feedback on their essays and research reports will also be minimal.

Until then, I’ve adopted a policy that students must include a link to their drafts’ histories so I can confirm the work is their own. I just finished reviewing the final essays from a 30 person course. I noted:

Getting students to properly share the links to their drafts’ history (from SharePoint or Google Docs) is a major nuisance: understandably, they forget or don’t set the permissions correctly.
Many students take only a few (2–5) hours the night before an assignment is due to write it—though they have the advantage of starting from a proposal.
The best work shows a progression from outline, to prose, and a pass or two of revision, over a few days.
Weaker students leave obvious AI tells in their work; sophisticated use would be undetectable.
I wonder if the cogent two-hour essays might be a transcription of already fabricated content.
This semester I had a handful of false-positives; that is, I thought prose was generated but I could see it was not in their revision histories. I’m relieved this policy prevented me from making false accusations.

A history of the advice genre on Reddit

2025-02-05T00:00:00-05:00

A new paper, and the first in work that will result in a book, chronicling the emergence of the advice genre on Reddit.

Reagle, J. (2025). A history of the advice genre on Reddit: Evolutionary paths and sibling rivalries. In First Monday. https://doi.org/10.5210/fm.v30i2.13729 (author’s copy)

ABSTRACT: Though there is robust literature on the history of the advice genre, Reddit is an unrecognized but significant medium for the genre. This lack of attention, in part, stems from the lack of a coherent timeline and framework for understanding the emergence of dozens of advice-related subreddits. Noting the challenges of Reddit historiography, I trace the development of the advice genre on the platform, using the metaphors of evolutionary and family trees. I make use of data dumps of early Reddit submissions and interviews with subreddit founders and moderators to plot the development of advice subreddits through the periods of subreddit explosion (2009–2010), the emergence of judgment subreddits (2011–2013; 2019-2021), and the rise of meta subreddits (2020–2023). Additionally, I specify a lexicon for understanding the relationships between subreddits using the metaphor of tree branches. For example, new subreddits might spawn, fork, or split relative to existing subreddits, and their content is cultivated by meta subreddits by way of filtration, compilation, and syndication.

Toilet paper orientation

2024-08-01T00:00:00-04:00

Does the next sheet of toilet paper belong under or over the roll? While working on Dear Internet, I noted that Wikipedia’s article cites a mistaken statement in the Guardian’s review of the controversy. This is said to be the most popular Ann Landers’ column ever — and perhaps of all advice columns — but I’ve never seen any reference to the actual column. Instead, Wikipedia refers the Guardian’s 2021 story which mentions a 1986 speech in which Landers said the issue generated fifteen-thousand letters.

I decided to trawl the archives and find the earliest instances of Ann Landers’ columns on the topic, which were in 1977 — you can find them here. Oddly, I couldn’t find the original columns in archives of Chicago Sun-Times, Landers’ home newspaper. Fortunately, the Boston Globe has excellent archives.

I also improved the Wikipedia article with these citations.