Understanding Q & A in the Age of AI

Introduction: Distinguishing Genuine from Performative Q&A

In the 1970s, sociolinguist Hugh Mehan discovered a peculiar discursive structure governing classroom interaction that he termed the Initiation-Response-Evaluation sequence. Teachers asked questions to which they already knew answers, students attempted responses, and teachers evaluated correctness. This IRE pattern revealed classroom “questions” as assessment mechanisms rather than genuine information-seeking, with the knowledge asymmetry operating backwards—questioners possessed superior knowledge to responders.

Mehan’s discovery matters because it identifies what Q&A is NOT. Authentic question-and-answer protocols possess a different structure where questioners lack knowledge and direct inquiries toward those positioned to provide it. This straightforward Q&A practice has organized human knowledge-seeking for millennia, from ancient oracles channeling divine wisdom through Enlightenment scientists establishing expert credentials to digital platforms connecting millions seeking answers from knowledgeable strangers.

This essay traces that long history of Q&A across technological and social transformations, examining how societies have structured the asymmetric relationship between those who ask and those who answer. That history now faces unprecedented disruption as artificial intelligence systems collapse centuries of evolving Q&A practice into private algorithmic consultation, altering forever how humans seek and receive answers to their questions.

Defining Genuine Q&A

Question-and-answer practices possess a deep history spanning from ancient oracles to modern artificial intelligence. Yet not every information-seeking behavior qualifies as Q&A discourse. Distinguishing Q&A protocols from similar but different practices requires attention to essential structural criteria.

The Five Essential Characteristics

A Q&A protocol exhibits five necessary characteristics. First, it originates from an authentic information need—someone lacks knowledge they require. Second, the question is explicitly directed toward a designated responder or system. Third, the questioner expects a response tailored to their specific situation. Fourth, an asymmetric knowledge relationship exists between questioner and responder. Fifth, the response attempts to close the identified knowledge gap.

What Masquerades as Q&A

Although nothing matches the inverted Q&A protocol Mehan identified in classrooms where the expert asks questions while the ignorant answer, many information-seeking practices superficially resemble Q&A but lack essential criteria. Reference works like encyclopedias provide pre-existing content requiring search-and-match behavior rather than tailored responses. Instructional content such as tutorials offers generic guidance created before any specific question arose. Traditional search engines retrieve matching documents but generate no answers themselves. General conversation may include questions organically but lacks the formal structure of designated expertise. Broadcast information systems deliver identical content to all recipients without query-response mechanisms.

The Critical Distinction

Q&A creates new discourse addressing specific inquiries. The responder—whether oracle, advice columnist, forum expert, or AI—generates tailored answers. Reference lookup, by contrast, matches queries to existing information. This distinction matters when examining how societies position those who lack knowledge to learn from those who possess it—or at least claim to possess it.

Blurred Expertise in Ancient Q&A Protocols

Ancient Q&A protocols depended upon intermediaries who claimed supernatural rather than empirical knowledge that could be validated through systematic observation. Oracles at Delphi channeled divine wisdom through cryptic pronouncements that addressed urgent political and military questions from anxious city-states. Shamans entered ecstatic trance states to access spirit knowledge that was unavailable to ordinary people seeking answers about misfortune. These practitioners occupied genuine expert roles within their societies despite considerable modern skepticism about their purported supernatural sources.

The asymmetric knowledge relationship remained even when the knowledge source was supernatural rather than human. Questioners lacked information they needed for critical decisions about warfare, agriculture, marriage, or healing, while oracles claimed privileged access to divine intelligence. Chinese emperors consulted oracle bones inscribed with questions and heated until cracks appeared, which diviners interpreted as ancestral responses. Tarot readers claimed symbolic systems revealed hidden patterns governing questioners’ lives and futures. These practices maintained the Q&A function and structure—informational need, designated expert responder, tailored answer—despite operating through metaphysical rather than observable phenomena.

Trust in supernatural experts derived partly from unfalsifiability since divine knowledge operated beyond empirical verification or contradiction through observable evidence.

Renaissance Investigation: Questions Without Experts

The Renaissance marked a dramatic shift in Q&A protocols as natural philosophers began interrogating physical reality when traditional authorities proved inadequate. Leonardo da Vinci exemplified this approach by systematically asking questions no existing expert could answer through supernatural or textual authority alone.

Leonardo observed the moon and theorized that it reflected sunlight rather than generating its own luminescence—contradicting prevailing beliefs of his era. He questioned how birds achieved flight and documented relationships between wing shape, air pressure, and the mechanics of elevation. When he wondered why ancient shells appeared atop mountains, his observations led him to conclude that these peaks had once been underwater—a geological insight centuries ahead of accepted theory. He dissected cadavers to understand how the heart functioned, observing it was a muscular pump rather than the furnace described by classical authorities.

The Renaissance emphasized asking questions for which acknowledged experts simply did not exist because investigation had not yet produced specialized knowledge. Natural philosophers became their own answerers through direct observation, experimentation, and systematic documentation of physical phenomena.

Enlightenment Systematization: Manufacturing Expertise

The Enlightenment continued this questioning tradition but transformed how societies produced authoritative answerers to increasingly specialized inquiries. Universities expanded beyond theology and law to establish natural philosophy, chemistry, and mathematics as distinct domains requiring formal training. Scientific societies like the Royal Society created mechanisms for validating expertise through peer review and experimental replication. Encyclopedias systematically organized expanding knowledge while identifying experts qualified to answer questions within specific domains.

This period fortified institutional structures that produced recognized experts through apprenticeship, formal education, and credentialing systems that persist today.

Professional Gatekeeping and Popular Alternatives (1800s-1950s)

The nineteenth century witnessed the professionalization of expertise as licensing systems legally monopolized certain question-answering domains. Medical boards restricted who could answer health questions for compensation, bar associations controlled legal advice, and engineering societies certified technical consultants. These credentialing mechanisms established formal gatekeepers determining whose answers carried institutional authority and legal protection.

Yet this same period produced alternative Q&A channels for questions that formal experts either couldn’t address affordably or considered beneath professional dignity. Newspaper advice columns emerged as accessible forums where “Dear Abby” and “Ann Landers” answered questions about etiquette, relationships, and domestic concerns. These columnists operated without formal credentials in psychology or sociology yet commanded enormous audiences precisely because they addressed everyday problems that licensed professionals ignored.

This dual-track system—credentialed professionals for serious matters, accessible columnists for quotidian problems—shaped modern expectations about expertise hierarchies. Working-class families couldn’t afford to consult attorneys about minor disputes or psychiatrists about family tensions, but they could mail letters to advice columnists who provided guidance within their lived experience and common-sense frameworks rather than specialized professional knowledge.

The division reinforced class distinctions in knowledge access while simultaneously democratizing certain forms of expertise through mass media distribution of advice.

Electronic Media: Broadcasting Expertise (1920s-1990s and Beyond)

Radio transformed Q&A protocols by enabling real-time consultation with distant experts through call-in programs that broadcast individual exchanges to mass audiences. Shows like “Car Talk” allowed listeners to phone mechanics about vehicle problems, receiving personalized diagnostic advice while thousands of others learned from overheard consultations. Medical call-in programs connected patients with physicians for preliminary assessments. Gardening experts addressed specific questions about particular plants in particular climates.

These programs maintained genuine Q&A structure—callers had information needs, experts provided tailored responses—while adding a broadcast dimension that turned private consultation into public education. The technology democratized expert access beyond geographic constraints while creating entertaining content from others’ information needs.

Telephone hotlines extended this pattern into private rather than broadcast channels. Suicide prevention hotlines, consumer help lines, and technical support numbers connected questioners with trained responders who provided immediate guidance. These systems scaled human expertise through organizational infrastructure—multiple answerers sharing similar training could handle volume that individual experts couldn’t manage alone.

The broadcast and hotline models both preserved human expertise while dramatically expanding accessibility beyond face-to-face consultation or printed correspondence.

Digital Networks: Distributed Expertise (1979-2005)

Pre-web digital networks experimented with asynchronous Q&A that separated questions from answers temporally while connecting geographically dispersed communities. Usenet newsgroups organized around specific topics allowed users to post questions that might receive answers hours or days later from anyone monitoring that particular discussion space. Bulletin board systems enabled similar exchanges within local dial-up communities before internet connectivity became widespread.

These early digital Q&A systems revealed new participation patterns. Unlike radio call-ins with designated experts, newsgroups operated through emergent expertise where knowledgeable volunteers self-selected into answerer roles based on their ability to contribute useful information. No formal credentials verified expertise—community judgment through subsequent discussion validated or challenged answers.

Ask Jeeves (1997-2006) attempted to bridge traditional search and Q&A by allowing natural language questions and providing curated “knowledge capsules”—pre-written answers to anticipated common questions. When queries didn’t match existing capsules, the system defaulted to traditional search results. This hybrid approach acknowledged the appeal of Q&A discourse while relying primarily on reference lookup rather than tailored response generation.

The evolution from Usenet through Ask Jeeves demonstrated growing sophistication in organizing distributed knowledge while revealing persistent tension between scaled Q&A systems and the personalized responses that define question-answering protocols.

Yahoo Answers: Gamified Expertise Without Credentials (2005-2021)

Yahoo Answers democratized Q&A protocols by eliminating formal credentialing while creating artificial incentive structures to motivate participation without monetary compensation. Users received 100 points upon registration and lost five points for each question asked, deliberately discouraging question proliferation while encouraging answer provision. Best answers earned ten points, advancing users through seven ascending membership levels that granted expanded privileges and community status recognition.

This gamification worked initially because it converted altruistic knowledge-sharing into competitive achievement while maintaining the asymmetric expert-novice relationship despite lacking verified expertise. Users with high point totals and authority scores functioned as recognized experts within topic categories based purely on demonstrated participation patterns rather than external credentials or validated knowledge.

The platform’s broad scope covering everything from cooking to philosophy to relationship advice reflected its inheritance from newspaper advice column traditions rather than specialized professional consultation. Questions ranged from practical (”How to get rid of skunk smell?”) to philosophical (”What is the meaning of life?”) to absurd entertainment that eventually dominated the platform’s cultural identity.

Yahoo Answers ended in May 2021 as declining usage reflected shifts in how users sought information through increasingly sophisticated search engines and emerging AI assistants. The platform became known more for absurd questions and entertainment value than serious knowledge exchange, degrading its utility for genuine information needs. Verizon cited “changing member needs” when shuttering the service, acknowledging that sixteen years of accumulated Q&A content would be permanently deleted rather than archived, eliminating a substantial corpus of human question-answer patterns from public accessibility.

Stack Overflow: Credentialing Through Demonstrated Competence (2008-present)

Stack Overflow emerged as a specialized Q&A platform exclusively for programming questions, implementing rigorous quality standards that contrasted sharply with Yahoo Answers’ entertainment-tolerant approach. The platform built reputation systems that validated expertise through demonstrated technical competence rather than gamification points. Users earned reputation through community judgment—upvotes on answers and questions—with higher reputation unlocking moderation privileges that let experienced members enforce quality standards.

Unlike Yahoo’s anything-goes approach, Stack Overflow aggressively closed duplicate questions, demanded specific technical details in problem descriptions, and expected answers to include working code examples. This strictness created a curated knowledge base where answers demonstrated verifiable technical accuracy rather than opinion or speculation. The voting system allowed peer experts to validate correctness rather than relying on the original questioner’s judgment about which answer proved most helpful.

Stack Overflow’s model represented expertise validated through community consensus within specialized domains—a middle ground between institutional credentialing and Yahoo’s participation-based recognition. Users with high reputation scores gained acknowledged expertise through demonstrated problem-solving ability visible to the entire community. This transparency allowed others to audit answer quality and challenge incorrect solutions through comments and alternative answers.

The platform’s success demonstrated that online Q&A could maintain high quality standards through community self-governance rather than formal credentials or institutional gatekeeping. By 2022, Stack Overflow had accumulated over 23 million questions and 34 million answers, creating the world’s largest repository of programming problem-solutions—until AI disruption dramatically altered its usage patterns.

The AI Disruption: From Public Exchange to Private Consultation (2022-Present)

ChatGPT’s release in November 2022 triggered rapid decline in certain online Q&A communities, though the impact varied dramatically across platforms. Stack Overflow, optimized purely for transactional information exchange, experienced severe disruption. The platform lost approximately three-quarters of its question volume since ChatGPT’s launch, with particularly steep declines in widely-used programming languages like Python and JavaScript.

This wasn’t just beginners abandoning the platform. Experts stopped participating too, and the questions that remained weren’t lower quality. They simply became less frequent across all skill levels. ChatGPT wasn’t replacing bad questions with nothing. It was replacing all questions—good and bad—with private conversations nobody else could see.

Why Some Communities Survived

The collapse hit different communities differently, revealing that platform design matters as much as AI capability. Programming languages that lots of people use saw huge drops in questions. But specialized corporate technologies that fewer people discuss publicly held steady, since ChatGPT learned primarily from public conversations and knows less about obscure proprietary systems with restricted documentation.

More revealing was the difference between platforms. Stack Overflow crashed while Reddit communities discussing identical programming topics barely changed at all. The difference? Reddit users came for social connection and conversation, not just information. When your Q&A platform is purely transactional—ask, answer, leave—an AI that answers faster will destroy it. When people participate because they enjoy the community, AI can’t easily replace that experience.

What Changed in How We Ask Questions

Traditional Q&A platforms required real work. You had to write a clear question, wait for someone to notice it, maybe answer follow-up questions to clarify what you meant, then evaluate multiple answers to figure out which one actually solved your problem. This entire process happened publicly where everyone could see it. Your question became searchable, so months later someone with the same problem could find your conversation and learn from it.

ChatGPT eliminates all of that. You type an unclear question and it figures out what you probably meant. You get an answer instantly instead of waiting hours or days. If the answer doesn’t work, you just ask for clarification immediately in the same conversation. But—and this is crucial—none of this happens in public. Your question, the answer, your follow-ups, the whole learning process stays private between you and the AI.

This resembles ancient oracles more than modern community platforms. You went to Delphi, asked your question privately, got your cryptic answer, and left. Nobody else learned from your exchange. As one Stack Overflow veteran put it: “What happens when we stop pooling our knowledge with each other and instead pour it straight into The Machine?”

The Public Knowledge Problem

Here’s the fundamental shift. ChatGPT learned how to answer programming questions by reading millions of Stack Overflow conversations. From 2008 to 2022, programmers posted their problems publicly and experts answered them publicly. AI companies captured all of that—every question, every answer, every clarification—and used it to teach their models how humans ask questions and how experts solve problems.

But now that ChatGPT is good at answering questions, people don’t post on Stack Overflow anymore. They ask ChatGPT privately instead. Those private conversations may be used to train future AI systems—companies like OpenAI do use user conversations for model improvement unless users opt out. But those conversations aren’t publicly searchable or accessible to other humans. They don’t build a shared knowledge commons.

Think about what’s happening:

Old system: You ask Stack Overflow, an expert answers publicly, everyone can see it, future humans searching Google can learn from it. AI can train on it.

New system: You ask ChatGPT, it answers privately, only you see it, future humans with the same problem can’t benefit, no public knowledge base grows.

The AI learned from fifteen years of public Q&A exchanges. But its success now stops new public Q&A exchanges from being created. It’s not that AI will run out of training data; all those Stack Overflow conversations from 2008-2022 still exist, and AI companies can use private conversations for training. The problem is that almost no new examples of human problem-solving are being made publicly available for other humans to discover and learn from.

Conclusion: Inverting the Inversion

Hugh Mehan’s IRE pattern revealed how classrooms inverted Q&A protocols—teachers asked questions they already knew answers to, testing students rather than learning from them. This performative questioning served pedagogical purposes but abandoned the fundamental asymmetry where ignorant questioners seek knowledge from expert responders.

Now imagine inverting Mehan’s inversion to restore Q&A within educational settings, but adapted for the AI age.

A Genuine Q&A Classroom

Students receive reading assignments but instead of preparing to answer teacher questions about the material, they prepare to ask questions about it. Their homework is identifying what excites them, what m makes them curious, what confuses them, what connections they can or can’t make, what implications puzzle them or need exploration. The assignment isn’t “read Chapter 3 and be ready for a quiz.” It’s “read Chapter 3 and come prepared with questions you can’t answer on your own.”

Students then work in small groups, posing their questions to each other first. When peer knowledge proves insufficient, they consult AI systems like ChatGPT—but with a critical mandate. They must evaluate the AI’s responses together. Does this answer make sense? Does it contradict what the text actually says? Can we verify this through the reading? What would we need to know to judge whether this response is correct?

The teacher collects the questions that survived peer and AI consultation—the ones that remain genuinely puzzling or where AI responses seemed questionable. These questions become the day’s curriculum. The teacher poses them to the full class, invites student responses based on their small-group deliberations, and then performs the evaluative role—but now evaluating collective understanding rather than individual performance. The teacher reinforces accurate consensus, corrects misunderstandings, reveals why certain AI responses were misleading, and models how experts think about these questions.

What This Model Addresses

This inverted-inversion directly confronts the problems AI has created for knowledge exchange. It makes student learning public again rather than trapping it in private AI chat histories. It makes private Chat histories public and exposed to scrutiny. When students wrestle with questions together and share their AI consultations, they recreate the kind of community-validated knowledge exchange that made Stack Overflow valuable.

It teaches students to evaluate opaque expertise. Since ChatGPT is a black box offering confident answers without visible reasoning, students must learn to question those answers collectively—developing the critical judgment that Stack Overflow’s voting systems once provided through community consensus. They learn that “the AI said so” isn’t sufficient justification, just as “the oracle said so” eventually proved inadequate for Renaissance natural philosophers.

It restores asymmetric knowledge relationships to their proper orientation. Students occupy the questioner role with real information needs. Teachers occupy the expert role, not just possession of correct answers. The AI becomes a tool whose outputs require expert interpretation rather than a replacement for expert judgment. The teacher stands ready to be a trusted resource during moments of need for a trusted resource.

Most importantly, it acknowledges that when everyone has instant access to information-generating systems, the critical educational task isn’t memorizing answers. It’s learning to ask good questions, evaluate questionable answers, and participate in collective knowledge-building.

This model faces practical challenges—time constraints, assessment pressures, varying student engagement levels, and teachers’ own need to learn AI evaluation skills. Yet these challenges seem manageable compared to the alternative: allowing private AI consultation to fully replace the public, evaluative learning that has characterized human knowledge-seeking for millennia.

The Historical Arc Closes

From ancient oracles through Enlightenment credentialing to digital community platforms, genuine Q&A protocols have organized how societies connect those who lack knowledge with those who possess it. Each technological transformation—writing, printing, telecommunications, the internet—changed the scale and accessibility of these exchanges while preserving their fundamental structure.

AI threatens to sever that structure by privatizing knowledge exchange and obscuring expertise behind algorithmic black boxes. But the same technology that created this crisis might also enable its resolution. If we can teach students to use AI as a tool requiring expert evaluation rather than a replacement for expert judgment, we preserve centuries of Q&A tradition while adapting it to new technological realities.

The question-and-answer protocol has survived shamans and oracles, Renaissance investigation and Enlightenment systematization, newspaper columns and radio call-ins, Usenet forums and Stack Overflow communities. Its survival now depends on whether we can restore the public, evaluative dimension that AI consultation has eliminated—making visible again the question-asking, answer-seeking, judgment-exercising practices that define genuine learning across all of human history.

Notes on my writing process

Research Methodology

This essay emerged from a hybrid research and composition process that mirrors the Q&A protocols it examines. The Mehan insight represents a long-standing conundrum; I’ve long recognized it as a distortion of Q & A, but I haven’t found a need to probe the Q & A genre until now as AI puts it at every learner’s fingertips. Initial research supporting this writing began with CSU Sacramento library database searches targeting peer-reviewed studies published between 2000-2025 addressing question-and-answer genres and their manifestation on the internet. These academic sources—particularly Gyöngyi et al. (2008), Khansa et al. (2015), Liu et al. (2024), and Metzler et al. (2019)—provided empirical data about user behavior patterns, platform mechanics, and community dynamics on Yahoo Answers, Stack Overflow, and related Q&A systems.

Reading these foundational studies generated secondary questions requiring further investigation: How did Q&A protocols function before digital platforms? What role did expertise play across different historical periods? How did earlier technological transformations affect knowledge exchange? These questions were explored using Perplexity, an AI-powered research assistant.

I found little information relevant to the Renaissance, which surprised me. I had an epiphany thinking back on what I learned from Walter Issacson’s biography of Leonardo Da Vinci, a read which took my breath away. Leonardo’s questions went far beyond the expertise of his age. What did he do? Instead of going to an Oracle, he asked the Earth to teach him. He took notes. He kept journals. Intrigued by a dearth of material about Q & A existing as a recognizable phenomenon during the Enlightenment, what I’ve long known about the university system became relevant in this new micro context. The Enlightenment was all about creating the social structures that would supply the kind of expertise Leonardo needed to answer his questions.

The decision to extend the historical analysis backward to ancient oracular practices emerged organically from recognizing structural similarities between contemporary AI consultation and supernatural intermediaries—both operate as opaque black boxes claiming specialized knowledge.

With the structure discerned from the arc of the emerging story, the drafting process itself employed a modified Q&A protocol. Each major section began with explicit questions that needed answering to construct the argument: What defines genuine Q&A versus superficial resemblance? How did ancient societies structure expert-novice relationships? What made Yahoo Answers’ gamification effective initially? Why did Stack Overflow survive longer than Yahoo Answers? How specifically has ChatGPT disrupted public knowledge exchange? These framing questions then guided targeted research using multiple tools—library databases, Perplexity, web searches for historical sources, and Claude (an AI assistant) for synthesis, clarification, and iterative revision.

This hybrid methodology acknowledges that contemporary research and writing increasingly depend on AI tools while maintaining human direction, critical evaluation, and intellectual frameworking. In this case, what took a day’s work to write (I had been collecting academic articles on Q & A for a different project which suddenly became relevant here so I don’t count that library work as time in this work) would likely have taken at least two days.

Human Responsibilities (Author):

• Identified the research question and conceptual framework linking Mehan’s IRE pattern to contemporary AI disruption

• Conducted initial literature searches through CSU Sacramento library databases

• Formulated all primary arguments and the essay’s organizational structure

• Generated secondary research questions based on gaps identified in initial sources

• Made all decisions about which AI-generated content to incorporate or reject

• Verified all factual claims, statistics, and quotations against original sources

• Wrote and revised all final content, maintaining editorial control throughout

AI Tool Assistance:

• Perplexity AI (December 2024-January 2025): Used to explore secondary research questions about historical Q&A practices, particularly regarding pre-digital systems and technological transformations. Generated background information that was then fact-checked against primary sources.

• Claude (Anthropic) (January 2025): Used for three specific purposes: (1) synthesizing information from multiple sources into coherent draft paragraphs, (2) identifying logical gaps requiring additional research, and (3) suggesting clarifications for complex explanations. All AI-generated text was edited, rewritten, or replaced to align with the author’s voice and argument.

Quality Assurance Process:
Every claim, statistic, and historical detail was verified against the cited primary sources listed in the references. AI-generated text served as draft material that underwent multiple rounds of human revision and fact-checking. I personally, intentionally selected every word of this essay and am pleased that it succeeds in fulfilling my intention.

References

Primary Academic Sources

del Rio-Chanona, R. M., Laurentsyeva, N., & Wachs, J. (2024). Large language models reduce public knowledge sharing on online Q&A platforms. PNAS Nexus, 3(9), pgae400. https://doi.org/10.1093/pnasnexus/pgae400

Gyöngyi, Z., Koutrika, G., Pedersen, J., & Garcia-Molina, H. (2008). Questioning Yahoo! Answers. Proceedings of QAWeb 2008, Beijing, China.

Khansa, L., Ma, X., Liginlal, D., & Kim, S. S. (2015). Understanding members’ active participation in online question-and-answer communities: A theory and empirical analysis. Journal of Management Information Systems, 32(2), 162-203.

Liu, Y., Tang, W., Liu, Z., Tang, A., & Zhang, L. (2024). Similar question retrieval with incorporation of multi-dimensional quality analysis for community question answering. Neural Computing and Applications, 36, 3663-3679. https://doi.org/10.1007/s00521-023-09266-6

Mehan, H. (1979). Learning lessons: Social organization in the classroom. Cambridge, MA: Harvard University Press.

Metzler, S., Günnemann, S., & Miettinen, P. (2019). Stability and dynamics of communities on online question-answer sites. Social Networks, 58, 50-58. https://doi.org/10.1016/j.socnet.2018.12.004

Contemporary Analysis and Reporting

Holscher, E. (2025, January 21). Stack Overflow’s decline. Eric Holscher’s Blog. https://www.ericholscher.com/blog/2025/jan/21/stack-overflows-decline/

Miller, P., & Rao, A. (2024, March). Generative AI degrades online communities. Communications of the ACM, 67(3). https://cacm.acm.org/opinion/generative-ai-degrades-online-communities/

Nixey, P. (2024, May 19). What comes after Stack Overflow? InfoWorld. https://www.infoworld.com/article/3988468/what-comes-after-stack-overflow.html

Stack Overflow. (2024). Stack Overflow Developer Survey 2024. https://survey.stackoverflow.co/2024/

TechXplore. (2024, September 25). ChatGPT’s rise linked to decline in public knowledge sharing on online Q&A platforms. https://techxplore.com/news/2024-09-chatgpt-linked-decline-knowledge-online.html

Historical and Platform Documentation

Ask.com. (n.d.). Wikipedia. https://en.wikipedia.org/wiki/Ask.com

Brinkmann, M. (2021, April 6). Yahoo Answers will cease to exist on May 4th, 2021. gHacks Technology News. https://www.ghacks.net/2021/04/06/yahoo-answers-will-cease-to-exist-on-may-4th-2021/

ChatGPT. (n.d.). Wikipedia. https://en.wikipedia.org/wiki/ChatGPT

Constine, J. (2023, August 16). Why everyone stopped asking Jeeves. Mental Floss. https://www.mentalfloss.com/article/94784/why-everyone-stopped-asking-jeeves

History of Ask Jeeves, Inc. (n.d.). FundingUniverse. https://www.fundinguniverse.com/company-histories/ask-jeeves-inc-history/

Leonardo da Vinci - Science and inventions. (n.d.). University of California Museum of Paleontology. https://ucmp.berkeley.edu/history/vinci.html

Perez, S. (2021, April 5). Yahoo Answers is shutting down on May 4th. Yahoo News. https://www.yahoo.com/news/yahoo-answers-shutdown-may-4th-210240460.html

Southern, M. G. (2021, April 7). Yahoo Answers shutting down May 4, content will not be archived. Search Engine Journal. https://www.searchenginejournal.com/yahoo-answers-shutting-down-may-4-content-will-not-be-archived/401553/

Yahoo Answers. (n.d.). Wikipedia. https://en.wikipedia.org/wiki/Yahoo_Answers

Yahoo Answers Scoring System. (n.d.). Quantarchive. https://answers.quantarchive.com/info/scoring_system

Research Tools Utilized

CSU Sacramento Library Databases: Academic Search Complete, JSTOR, ProQuest, Web of Science (accessed December 2024-January 2025)

Perplexity AI: Research assistant for exploring secondary questions, historical context, and cross-referencing claims (December 2024-January 2025)

Claude (Anthropic): AI assistant for drafting, synthesis, citation verification, and iterative revision (January 2025)

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Transparency Statement: This essay represents collaborative human-AI research and composition. The author conducted initial research, formulated all central arguments and questions, selected which AI suggestions to incorporate, verified factual claims against primary sources, and maintained final editorial control over all content. AI tools assisted with literature synthesis, historical research, drafting efficiency, and identifying logical gaps requiring additional evidence. All statistical claims and direct quotations were verified against cited sources. This methodological transparency reflects the essay’s own argument: AI should augment rather than replace human judgment in knowledge work.