AI & Education – Learning Aloud

The “you are doing it wrong” excuse and Classroom AI

There is a common perspective on the practice of education, intended as a criticism, I think, that proposes if a visitor from the past were to be time-traveled to the present, he or she would be amazed, but bewildered by so many areas of civilization (travel, medicine, farming), but feel completely at home in K12 or university classrooms. As an educational researcher, I admit this claim has always troubled me. Was the process of passing on knowledge and developing important skills optimized centuries ago despite all of the folks like me who study how people learn and how the processes supporting learning might be improved? If I disagreed, what would I identify as a counterexample, or how, at the very least, would I justify the time, effort, and resources people like me have invested in changing the status quo?

My Interest in Individualization

A general topic that has long been at the core of my personal research interests has been individualization. It seems obvious that learners differ on important variables that impact learning. Some have greater aptitude than others. Some, due to an endless list of differences in life experiences, at a given point in time have significant gaps in relevant background knowledge and prerequisite skills. For economic and historical reasons, our approach to assisting student learning largely ignores these differences. Our system, despite claims, fails to actually meet students where they are to most efficiently move them forward. Where students are also ignores differences in goals, interests, and whatever else might come under the general heading of motivation.

Those who have followed my posts over the years will likely recognize that much of what I have done has focused on evaluating approaches that make use of technology to expand the flexibility educational systems can practically offer. I will identify two such topics for those who might want to explore my past posts –mastery learning andtechnology-supported tutoring. I admit that these seemingly logical opportunities have not yet yielded the benefits in application I had hoped, and this is the topic I want to examine in this post.

Research in the social sciences which would include applied research in education (e.g., classroom learning) has notorious weaknesses, but unique challenges. For example, recent criticism offered to the public notes the high rate at which published research cannot be replicated. We seem in an era in which funding for science in general has been questioned, so with cutbacks those of us working in more challenging areas have reason to be concerned. Yes, I said more challenging. I agree the “basic sciences” are of great importance and deserve support, but think of the claim I used to offer when I taught the research section of Introduction to Psychology – the chemicals in the test tube, the electrons in the circuit, or the planets in space don’t think about how they feel like reacting today. The rules that explain such behaviors may be intricate and difficult to ascertain, but at least most are reliable. The challenges social scientists face are simply different, but the general trend has been toward greater understanding.

Back to the thought experiment about the visitor from the past

If one assumes progress should happen when it either hasn’t or at least not to the degree that seems reasonable, is there reason for optimism? Are optimists delusional? What are optimists up against when it comes to criticism of present practices and seeking funding and attention for new approaches?

Changing a massive system with highly ingrained beliefs and behaviors is tremendously difficult. New ideas struggle to take hold and mature within this environment. An “intellectual pessimism” used to resist deep exploration of theoretically logical and basic research justified changes I have decided to describe as the “you are doing it wrong” plea for continued experimentation. I don’t think you can search for additional references to this phrase expecting a lot of success, but it is a phrase I have decided captures the attitude I think typifies the resistance others have identified.

The phrase implies criticism of researchers who insulate themselves from scrutiny of their “big ideas” by attributing poor outcomes to implementation failures rather than to flaws in the ideas themselves. In other words, why do “big ideas” continue to resurface repeatedly over time when attempts to apply these ideas have not previously been successful. Perhaps most simplistic put, it is about excuses.

The “you are doing it wrong” explanation works like this. When a widely adopted educational innovation – learning styles, discovery learning, whole language reading, AI tutoring, open classrooms, etc. produces disappointing or mixed results, proponents rarely concede the theory is wrong. Instead they argue: the idea is sound, but practitioners didn’t execute it faithfully or well enough. The failure belongs to the implementers, not the framework.

This rationale functions as an unfalsifiable escape hatch. Any negative evidence gets reframed as a measurement of implementation quality rather than a test of the underlying idea. The theory can never lose, because every failure is a fidelity problem.

In education, common variants are typified by the following:

“Teachers didn’t receive adequate training” – used with constructivism, project-based learning, differentiated instruction, AI

“It wasn’t implemented with fidelity” – the research or theoretical components were not followed with sufficient care.

“The conditions weren’t right” – class sizes, demographics, resources, culture

“It was a watered-down version” – the pure form was never really tried

With these excuses, it is the grain of truth that makes it plausible. Of course, there is always the possibility that the excuse is valid. This pattern is worth naming clearly in writing about learning science, because it explains a lot about why education cycles through fashions without accumulating settled knowledge the way other applied fields do.

Are classroom uses of AI the most recent examples of “you are doing it wrong”?

AI applications in classrooms represent recent examples of promising innovations, but also potentially of an impotent fad (e.g., Gerlich). Claims of cheating instead of learning abound and while theory and carefully controlled research point to logical and demonstrated benefits it would seem fair to argue educators are concerned about most student use.

Recently, I have encountered multiple accounts that propose “you are doing it wrong”. I intend to develop an extended analysis of the core ideas of these claims in a future more analytical post, but I might quickly summarize here by explaining that true success in the use of AI is most likely when certain conditions of student motivation, metacognitive proficiency, and working memory issues are met. In many cases, these variables are not functioning at desirable levels.

Several writers have backed a three-level AI use model proposing that some levels (the lowest and the highest) are likely to be successful and the middle level, which is presently most common, is less likely to produce satisfactory results.

The three levels have the following characteristics:

Zone 1: No AI Involvement

In this level, learning occurs without any AI assistance. While learning happens, it is often “capacity-constrained” because the learner must spend significant time and effort on execution and task completion, leaving less bandwidth for higher-order reflection.

Zone 2: Scattered, Half-Hearted Use

This is characterized by using AI for minor tasks like fixing sentences, checking facts, or tidying paragraphs. It often produces the worst learning outcomes. The learner still carries nearly the full cognitive load but adds the overhead of managing AI interactions without gaining significant cognitive savings. Note: this summary paraphrases the description of the authors. My version would add having the student using the AI tool to perform the task based on simplistic instructions.

Zone 3: Committed, Strategic Delegation

This level involves offloading entire categories of substantive work to AI to free up genuine cognitive capacity. This freed bandwidth is then redirected toward tasks AI cannot do, such as critiquing frameworks, questioning assumptions, and making complex judgment calls. This zone is where “transformative learning” is thought to live, provided the course design is intentional about how and why tasks are delegated.

My suggest for making sense of these differences is to take a familiar task and work through what these different zones might look like in practice. I think learning to write makes a good case.

In this example, Zone one is easy – no use of AI. Zone two might include simply asking the AI tool to complete an assignment for you or perhaps using a tool such as Grammarly to check spelling and grammar. What would Zone three look like? Perhaps you might use an AI tool to suggest a list of topics you might address based on your general goal or perhaps create a draft and then ask the AI for a critic based on concerns you have about your initial effort.

Summary

I hope it is obvious how “you are not doing it right” would apply to how educators may allow students to use AI. The challenge then to evaluate whether such uses are an example of a typical educational fad or actually are limited because the learner is not doing it right.

Source

Gerlich M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies. 15(1), https://doi.org/10.3390/soc15010006

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.

Ignoring The Instruction Option Of EdTech

When I first began writing professionally about K-12 use of technology in the mid-1990s, a popular approach was to organize content around the tutor, tool, tutee model. This model proposed that technology in the hands of students could deliver instruction (tutor), facilitate the activities of being a student (tool), and program/code (tutee). While AI now blurs the lines between these roles, this simple organizational scheme still seems useful.

This post was prompted by what I sense to be dissatisfaction with the instructional component of this model and a recent paper entitled the “5% problem. This paper challenged the positive benefits of commercial instructional offerings (e.g., Kahn Academy, CK-12) as misrepresenting what the data on achievement they have collected demonstrate. Ignore my descriptor of such programs as commercial when I know you can use at least many of the features of such offerings at no cost. How these efforts are funded is a different issue. The relevance of “5%” lies in the hidden expectation that only those who use the learning system as intended are included in the analyzed data. Some studies reporting high effectiveness are based on 5% of those provided access and this important factor is not highlighted in the reporting of results.

Such assertions make me uncomfortable. Despite what to me seems a backlash against screen time, cautions related to AI allowing learners to offload the experiences intended by learning tasks, and concerns classroom circumstances associated with technology have caused educators to limit meaningful social contact with students and students with each other, now I am feeling I must question the studies I have explored on the benefits of AI tutoring and the personalization of the rate of progress through instructional materials allowed by computer supported instruction (e.g., Kulik & Fletcher).

Teacher Commitment

As I have considered this recent challenge, it has occurred to me that I have encountered a variant of it throughout my career. In 2019, I wrote a blog post titled “There is a reason teachers don’t use the software provided by their districts.” At the time, this issue caught my attention because my wife and I were serving on an advisory group for our local school district and the tech director reported on a monitoring software used to track the use of software the district had purchased to make decisions about which license access packages could be dropped so funds could be reallocated to other requests. I noticed some researchers were using what seemed like a similar system to examine the use of instructional technology and to consider why it was underutilized. These scholars reached a conclusion nearly identical to that of the more recent, in-depth examination of online instructional tools. “One of the other primary findings of this report is that usage of apps is generally lower than might be expected. Most apps are used only for a limited time, and most purchased by districts go unused. This has an impact on efficacy – an app cannot be effective if it is not used” (p.25).

At that time, it seemed the issue was explaining teacher commitment. Thomas Arnett has weighed in on the issue of school-funded software being seriously underutilized, speculating, based on his Jobs to be Done Theory, that educators simply don’t perceive that the software they have access to helps them satisfy the jobs they perceive as expected of them, relative to more traditional approaches. These jobs are described as 1) Help me lead the way in improving my school, 2) Help me find practical ways to engage and challenge more students, and 3) Help me replace a broken instructional model so I can help each student. From my perspective, many technology-based instruction systems seem purposefully designed to address individual learning speeds and existing knowledge, but perhaps this is how these resources by educators. In a more detailed version of this only online description, these authors propose that educators might respond if a greater effort were made to engage educators with data and anecdotal accounts of the success of peer educators.

What about the learners?

As I explored this history and what seems a frustrating pattern for those of us who have been influenced by the seeming promise of personalized progress systems and intelligent tutoring systems in a carefully controlled context, when turned loose in the complexity of schools and classrooms. The challenge of matching key elements of the controlled setting in which concepts are developed in applied settings is termed fidelity and is an issue in many fields (e.g., Trustschel and colleagues). I have struggled with this challenge in my own research, which has often focused on creating technology-facilitated study environments for college students enrolled in large introductory classes.

Cognitive research has accumulated a massive amount of evidence demonstrating the effectiveness of retrieval practice and the challenge that less capable learners are often much less aware of their specific knowledge gaps and a false sense of understanding (i.e., metacomprehension). In other words, less capable learners often don’t know what they don’t know and thus are very inefficient at remediating their problem areas. One way to provide retrieval practice and address poor metacomprehension is to provide practice tests. More sophisticated applications that make use of technology can also track weak areas so that these areas can be emphasized, link the student to remedial content when individual elements of information are not known or misunderstood, and even request students to predict the accuracy of their performance in an effort to increase awareness of strengths and weaknesses.

If you are interested in the details of this study, I have provided a citation below. The relevance of this study for the present post concerns the willingness of learners, college students in this case, to take advantage of a resource designed to improve their performance. The following graph is an easy way for me to make my point. Learners were divided into three groups based on course performance. For each of the three exams, the percentage of learners in each performance group who satisfy the stated goal of the study task, use but do not meet this standard, or do not use the study task is identified. There is a clear pattern: those performing the worst do not meet the study goal. Most persuasively in keeping with the other data reported in this post is the data on those who made no effort to use the system. It is possible trying but failing to reach the stated standard is related to understanding or aptitude, but failing to try, which should still be beneficial, is not.

As was the case in the 5% paper, those less in need of assistance participated more in a likely beneficial activity. In fairness, the “perceived suitability” of a learning opportunity proposed while vague offers a second possible explanation.

Summary

In this post, I consider the persistent “underutilization gap” in educational technology, where instructional tools—from commercial platforms to AI tutors—frequently fail to achieve their promised impact because they are either ignored by teachers or avoided by the students who need them most. It is true that the “5% problem highlights how efficacy data is often skewed by only including the small fraction of users who follow the system as intended, while struggling learners consistently participate the least in these personalized systems. Ultimately, I suggest that EdTech’s potential for personalized progress remains stalled by a lack of “fidelity” in real-world settings and a failure to align software with the practical “jobs” educators and students actually prioritize.

Citations:

Grabe, M., & Flannery, K. (2010). A Preliminary Exploration of Online Study Question Performance and Response Certitude as Predictors of Future Examination Performance. Journal of Educational Technology Systems, 38(4), 457-472.

Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of educational research, 86(1), 42-78.

Trutschel, D., Blatter, C., Simon, M. et al. (2023). The unrecognized role of fidelity in effectiveness-implementation hybrid trials: simulation study and guidance for implementation researchers. BMC Medical Research Methodology, 23, 116. https://doi.org/10.1186/s12874-023-01943-3

Searching the Scientific Literature

My work has always required that I locate, read, and keep track of the content of scholarly papers – mostly journal articles. This is typical of those of us whose academic interests combine research with teaching the core ideas of a science-related field of study. My personal focus was educational psychology and, even more specifically, reading skills and study behavior. Over the years with this foundation, I became interested in the role technology could play in these same topics and most recently, including how technology can effectively be employed in the reading, processing, and application of information by independent learners (i.e, learners who guide their own learning outside of formal classroom settings).

Over the course of 50+ years, the means by which those of us with such interests have experienced many changes in how we locate, read, and keep track of the content that forms of the basis and sometimes the outlet for our work. We usually purchased the journals we could afford and perused others in our local library. We once had postcard-sized forms we used to send requests to researchers to see if they had free copies of papers they would return as a professional courtesy. When you published a paper the journal at one time would provide you 50 or so individual copies you would use to participate in this exchange. Libraries have always had limited budgets and some of the less popular journals might be purchased as microfilm or microfiche that could be used to guide personal notetaking or perhaps be connected to a coin-fed “xerox” machine. Now, there are many more journals and libraries that still have limited budgets may buy access to digital collections of journals that allow patrons to download PDFs.

A challenge then and now in this process is how one goes about finding the specific articles and chapters you would read and collect. Libraries used to subscribe to services that provided intricately organized periodicals that would attempt to label research studies. If you didn’t peruse the journals on the “just arrived” section or the shelves, you would try to use these periodicals to guess what labels had been used to identify the content you might want to find in the stacks of your library or send for. Which articles you found, you would use the “reference” section to identify related work that seemed promising. We still do this, but it only works to find documents that are older than the one you happen to be reading at the time. As technology played a more and more important role in organizing content, large databases were developed that could be searched first by matching key words and now with AI capabilities that can respond to prompts that do not have to rely on exact matches to specific words or phrases.

This bring me to my goal in this post. There are now many tools available to both academically affiliated and independent learners to find what they hope will be useful resources. Some of these tools will now go further in summarizing what is found and even attempt to apply what was found in the creation of papers for different purposes. I am most interested in the location. I want to read the documents for a variety of reasons that I think are important, but I do not intend to discuss. I also have access to a research library that allows me to download PDFs of documents so I don’t need a service that will do that for me.

So, to summarize, where this leaves me personally. I am now retired, but retain online access to library resources. I do not have an easy way to work with library personnel or the most powerful tools available if I could work directly from a library. I do not want to spend a great deal of money on what I guess I would call “search tools”, but I have spent a good deal of time exploring a variety of free or inexpensive tools. I want to share insights related to my own experiences.

Here is one issue that may not be obvious to those with access to more expensive tools or those with no reason to explore as I have. Most of the literature I am interested in is behind a paywall. Many probably have been exposed to issues related to this reality. Why can’t citizens who, in a way, pay for much of this research through their taxes, read what the research looks like and what it concludes? Who makes the money from this component of academic scholarship? The researchers don’t get paid by journals for their papers. They are expected to review submitted papers for publication to identify high-quality work without compensation. Where does the huge fees libraries pay for access to scientific journals go?

These issues aside, most search engines that scour the Internet for information that users can search for cannot typically access content protected by paywalls. My personal issue is this how can I efficiently identify useful sources to read. Others have an even greater challenge. How can those without a “faculty pass” learn what recent research has to offer?

My current approach

I currently make use of the following tools/services:

SciSpace is the only one of these options I pay a subscription service to use so the rest have a free level or do not charge for any of the services provided. Again, I only need to locate citations as I have full access to a research library and I am not under the immediate pressure of working on a thesis or dissertation.

Comments

For articles behind paywalls, Google Scholar is usually my best starting point. It provides citations (sometimes incomplete in my experience). It also lists other publications that have cited the item you have targeted, which can be very useful. The citations include links to the journals in which the articles are published, which provide the full abstract and may or may not allow downloading the full article, depending on the individual journal’s policy.

Long-time Google Scholar users who have not explored the Google Labs option for Scholar should take a look. Rather than search terms, you can ask research questions much as you would with an AI tool. This approach allows a user to identify key topics and related issues. So, to stay focused on searching for journal articles on cyberbullying, I could request articles that examine school programs to combat it. After evaluating the results, the system identifies relevant papers and explains how each paper addresses your request.

Semantic Scholar provides features similar to Google Scholar (see below), but I have found it less effective in identifying sources I know exist. Given the overlap with Google Scholar, I use this service much less frequently.

I use Research Rabbit once I have identified a source I find valuable. Research Rabbit will then surface other sources from this entry point and show the citation map of how these sources are connected. This is also somewhat redundant, but the interconnection graphs are interesting.

SciSpace is useful for semantic searching and summaries of the contents of papers that are located. It is my impression that it is a hit-and-miss tool for locating documents on paywalled journals and I would not depend on it for this purpose.

The following sequence of images shows the return from the prompt “What is the average daily writing time for K12 students?”. The tool responds with a summary based on the best sources found and provides access to specific information for the sources it identified. Often, a PDF is not available for paywalled sources, but a citation is available, allowing me to try to find that paper in some cases.

Perplexity can help you find references and surface source links, but it is a general web answer engine, so it is usually not the best choice for systematically searching scholarly journal literature. I do use it to offer insights into how I might address topics for which references are less important.

When access to a journal is not available

When you have identified an article that looks good but is paywalled, there are still things you can try. Scholars may post prepublication versions of papers elsewhere. Just try a traditional search using the title of the article you want.

Some official repositories of alternatives can be identified through Google Scholar. After identifying an article of interest, check whether the response indicates there are alternative versions.

In this case, one of the alternatives (see following image) identifies a secondary source as ResearchGate, and this repository offers a full pdf of the article the journal protects. These are not illegal copies so you do not have to hesitate to make use of this option.

Summary

For my purposes, which involve paywalled content, Google Scholar is usually the best starting point because it is broad and often surfaces publisher pages, institutional copies, and free versions when they exist. It also indexes paywalled articles themselves, so you can still discover the citation even when the full text is inaccessible.

Semantic Scholar is also strong for discovery, but it focuses on open-access options where available and is less oriented toward paywalled content than Google Scholar.

Research Rabbit is very good once you already have one paper or author and want related literature through citation chaining, but it is less of a primary search engine for broad paywalled journal discovery.

SciSpace is useful for semantic searching and paper summaries, but it is better as a literature-review assistant than as the main tool for hunting down paywalled journal records.

Perplexity can help you find references and surface source links, but it is a general web answer engine, so it is usually not the best first choice for systematically searching scholarly journal literature.

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.

A return to RSS to combat the changing media environment

The combination of recent acquisitions of major news outlets by extremely rich individuals in combinations these owners are have made some efforts to influence the tenor of programming and the topics covered is causing me some concern. I admit that it is difficult to operationalize story selection, objectivity, and even the target of humorists has become a topic of public discussion. I occasionally take a look at the data collected by organizations that attempt to measure political orientation (e.g., AllSides) and I see that recent news stories CBS, NBC, and the Washington Post are still listed as “leans left”. Part of the challenge here is that the companies making such determinations make use of public ratings rather than some more objective approach and public perception may influenced by accusations rather than facts.

Wealthy owners have almost by necessity controlled news organizations.

The Ochs-Sulzberger family, who own The New York Times.
The Murdoch family, owners of Fox News, The Wall Street Journal, and The New York Post

Newer wealthy owners largely come from the tech sector and are far more diversified in their financial interests.

Elon Musk, owner of X (formerly Twitter).
Mark Zuckerberg, whose platforms—Facebook, Instagram, WhatsApp—reach billions.
Larry Page & Sergey Brin, whose Google and YouTube dominate search and video.
Jeff Bezos, owner of The Washington Post and Amazon MGM Studios.
Larry Ellison, whose companies are expanding into CBS, Paramount, and potentially CNN and TikTok.

Much of my own understanding of major news outlets has been come from reading books focused on the history of the New York Times and Washington Post. For example, my most recent read was Marty Baron’s recent book describing his personal history with the Washington Post (Collision of Power: Trump, Bezos and the Washington Post). I think it fair to suggest that journalism as an ideal is about accurately presenting the facts and there is a constant tension within the professional between this goal and pressures to interpret and motivate.

Recent analyses of online news traffic, ownership structures, and platform consolidation reveal a trend that should concern anyone who cares about democracy, transparency, and the free flow of information. So, are billionaires taking over the news media? The evidence increasingly points toward “yes,” though the story is more nuanced than simple takeover headlines suggest. How objective is the news that we consume and have the few who control what we consume biased what is available to the public?

Why Are Billionaires Buying Up Media?

The motivations vary and are speculative, but here are several of the proposals:

1. Influence, Not Journalism

Owning a media outlet means controlling—not just influencing—the agenda. Which issues get amplified, which voices get sidelined, and which perspectives become “mainstream” can all shift at the whim of ownership.

This happens both subtly, through editorial pressure, and overtly, through firings, policy changes, and platform algorithms.

2. Protecting Business Interests

Billionaires with vast non-media empires benefit from shaping political and public opinion.

A critical news investigation can spark antitrust scrutiny, regulatory action, labor pressure, or public backlash. Owning the outlet that might publish such investigations can conveniently soften that blow.

3. Political Leverage

When politics and media ownership mix, journalism suffers. Several media owners have cultivated close ties to political leaders—particularly U.S. presidents. This relationship can lead to:

favorable regulatory rulings,
advantageous business deals,
less oversight,
and more power.

The public rarely sees this influence—but journalists inside the newsrooms often do.

4. Prestige and Legacy

Historically, newspapers have been considered markers of influence and intellectual status. For many billionaires, buying a publication is as much about image and legacy as profit.

Journalism’s core purpose – to hold the powerful accountable – cannot thrive in an environment where the powerful own the press.

Who is at fault?

The unsettling part is that present trends are probably not as devious as might be assumed and are a result of the following:

failing local news revenues,
the collapse of newspapers,
tech giants becoming gateways to information,
and billionaires filling the vacuum because they could.

The result is the same: a public sphere increasingly shaped by private interests.

We all bear some responsibility if we rely on “free” social media for news and have given up our subscriptions to newspaper (even digital) and magazines. The combination of free in terms of money and less demanding in terms of the effort to read longer form content is a bad combination. Of course free isn’t really free. We offer our time and attention to those who provide information and we open ourselves up to the manipulation associated with displaying more ads and attention grabbing content fed to us by algorithms designed to attract our attention. Our personal biases and motivations are fed by these algorithms rather than objective journalism.

Of course, we are told to purchase national and local newspapers and read in general, but maybe this is too idealistic.

Try RSS Again

RSS (Really Simple Syndication) was popular in the early 2000s as a way for individuals to keep an eye on multiple sources such as blogs and news sites to see if those sites contained any new material. Instead of taking the time to go to each site to check, a news reader kept track for you and offered a way to scroll through a list of updates to select sites you wanted to revisit. You were in control of both the sites you followed and the updated material you took the time to view.

RSS became less popular when Facebook, Twitter, and other social sites with feeds made it easier to find new content in their feeds, which featured what others found interesting and useful. However, it wasn’t really everything. Algorithms selected what appeared in our feeds and what appeared at the top. In a way, self-selection was replaced by algorithms.

RSS readers still exist – e.g., Feedly, Inoreader, Reeder. I use a new service called Current. The issue is how such services are used.

My Proposal

If the goal is to spend more time exposed to unbiased content, here is what I think makes sense. Identify sources based on solid journalism committed to a fact-first approach. I wondered if there was a way to avoid what might be my own biases or at least add solid sources to my personal biases. I decided to involve AI and I suggest others with goals similar to my own might want to take a similar approach.

Step 1: Prompt AI to identify the type of resource

My Prompt: What are the best unbiased news services for national news in the U.S?

*********

The AI response

A practical way to get “unbiased” U.S. national news

No outlet is perfectly unbiased, so the most reliable approach is to combine:

a straight-wire source (fact-first reporting), and
a bias-comparison / aggregation tool (to see how different outlets frame the same story).

Best “least-biased / straight reporting” services (great for national U.S. news)

Associated Press (AP / AP News) — widely used as a baseline “just-the-facts” wire style.
Sources: AP “About” (independent, nonpartisan), AP News
Reuters — another global wire service known for restrained, fact-forward reporting (excellent for politics, courts, and especially business/economy coverage).
(Reuters is commonly paired with AP in “least biased” recommendations; see also aggregator lists like PureVPN’s list for how it’s often categorized.)

Best “see multiple perspectives” services (reduces single-outlet framing)

AllSides — shows coverage “from the Left / Center / Right” and provides bias ratings; helpful for avoiding echo chambers.
Source: AllSides Balanced News
Ground News — compares how outlets across the spectrum cover the same story; useful for spotting omissions and framing differences.
Source: Ground News

Best “public-service style” national news (generally lower heat, more context)

PBS NewsHour — often recommended for measured tone and interviews/context (not a wire, but typically less sensational).
Mentioned in “least-biased” style lists like: PureVPN’s list
NPR — strong reporting and corrections culture; perceptions of lean vary by topic, but many people use it as a core national source alongside a wire.
(Also commonly listed in “low-bias” roundups: PureVPN’s list; and discussed in evaluation guides like UAA/APU library guide)

If you want a “single app/site” that tries to be neutral

Straight Arrow News (SAN) — positions itself as “unbiased / straight facts” and includes meta-coverage tools.
Source: Straight Arrow News

Quick starter stack (simple and effective)

AP or Reuters for your default daily read
AllSides or Ground News when something feels politically charged or confusing

If you tell me whether you prefer (a) fastest headlines, (b) deeper explainers, or (c) video, I can narrow this to the best 3–4 for your routine.

*****

Step 2: Create a convenient way to import these sources into the RSS Reader

It would be easy enough to take the list from Step 1, adding each recommendation manually. There is an easier way I thought might work given the coding capabilities of AI.

My Prompt: Create an opml file to access these sources

AI response

I have created the unbiased_news_feeds.opml file for you. It includes the “Top Stories” and “National” feeds for the sources we discussed: AP News, Reuters, NPR, PBS NewsHour, AllSides, and Straight Arrow News.

You can download the file directly from the interface and import it into any RSS reader (like Feedly, Inoreader, or NetNewsWire) to have a single, organized feed of U.S. national news.

How to use this file:

Download the .opml file from the chat.
Open your RSS reader of choice.
Look for “Import” or “Import OPML” in the settings or sidebar.
Upload the file, and your reader will automatically subscribe to all these sources at once.

If you’d like to add or remove any specific sources, just let me know!

*******

Here is the content of the OPML file.

<?xml version=”1.0″ encoding=”UTF-8″?>

<head>

<title>Unbiased U.S. National News Feeds</title>

</head>

<body>

</outline>

</body>

</opml>

If you want, you can copy and paste the OPML content that appears here. Save it as a text file ending in .opml – e.g., balancednews.opml. If you are already an RSS reader user or want to try one of the tools I listed above, all you need to do is import this source, and you will be following the news sites I follow.

To complete the process, here is what I would do with my reader.

Current offers several ways to add sources. In the image above, my existing sources appear (all education blogs). At the bottom, there is an Import OPML button that allows the AI-created OPML file to be uploaded.

Summary

It seems reasonable given the politicization of news to exercise greater control of the content each of us consumes. News organizations seem less independent. The RSS reader approach allows this control if the sources identified by AI meet the requirements I specified. Hopefully, the description offered here are sufficient to give others what is needed to duplicate my approach.

AI reduces skill learning

When a technology offers advantages and disadvantages, the decision-making process can be quite complicated, especially when oversight cannot be guaranteed. For example, many states now ban cell phones, making a use such as telling parents when the schedule for after-school activities has changed difficult. The advantages and disadvantages vary with the field of application and my interests have mainly been focused on education. Just to be clear, by this I mean learning in general, not just the type of learning that occurs under supervision or is associated with educational institutions.

The generic educational situation that raises concern involves tasks undertaken to encourage both skill development and knowledge, and includes a requirement that demonstrates that the task has been attempted by the existence of some product. In educational settings, such products might result from homework or class activities, or simply by visible demonstrations of activity. The issue with AI is that in many cases, such as problem sets or documents of various types, these same products could be generated by AI, avoiding the cognitive activity of the learners. The phrase “cognitive offloading” has been used to describe this alternative form of product creation. Teachers might simply call it cheating. Cognitive offloading itself can be a desirable or undesirable option, requiring decisions regarding when it is appropriate and efficient, and when it is a detriment.

While cognitive offloading to avoid learning tasks seems an obvious problem, little actual research exists to demonstrate the damage done. Some would argue that if technology can replace an activity and that technology is readily available, why bother to “learn” the skill in the first place? Why learn information if your cellphone can allow you to search for information when it is needed? Why learn basic calculation skills when you cellphone can also serve to do mathematical operations? There are responses to these challenges, sometimes offered by students or parents, but this analysis would take this post in a direction I did not intend.

Here, I want to focus on learning to write and writing to learn by discussing a different learning task. This may sound unnecessary, but at present, there is a reason to take this approach. The justification for being indirect is that writing is a complex skill consisting of multiple subskills, and we learn to become competent at even a basic level over years and not weeks or hours. We are investigating an alternative to the traditional methods of instruction that can be subverted now, and we cannot rely on experience to help us evaluate and tease apart how the development of subskills are impacted. The insights and evidence of the potential damage done would take to long to emerge. As one perspective, consider the lingering impact of COVID on learning. What about the move to online learning did we not anticipate and what consequences are we still trying to mitigate?

AI in Learning to Code

Shen and Tamkin had an opportunity to investigate the impact of AI with adult programmers learning to make use of a new library. Think of a library as a collection of functions (tools to perform specific and commonly used tasks). Instead of having to write code to accomplish common tasks each time a programmer encounters a need, libraries allow programmers to call prewritten code snippets. It takes some work to make use of a library – what functions are available, how do you call the function you want, what inputs and outputs are involved and how are these integrated with the code you write yourself? The researchers recognized that the learning coders had to do to make use of a new library provided an opportunity to study how AI could help and hinder learning a complex process.

Shen and Tamkin studied actual programmers as they worked to learn a new library. They suggested that the process be viewed as a tutorial including both background information and simple programming tasks. Programmers were assigned to a control and a treatment group, with the treatment group having access to AI. The learning phase concluded with an assessment evaluating multiple concepts and skills. Video of treatment group participants was collected to document how each individual used AI and worked on the programming exercises.

The researchers found that the treatment groups did not differ significantly in the time spent learning, which they found surprising. On the post-test, the largest group differences were in debugging skills. Smaller skill differences were found for code reading and conceptual understanding. Those without access to AI made more coding errors on the practice tasks, spent more time practicing debugging, and ended up with better skills on the outcome evaluation. How AI was used differed greatly with some simply asking AI to solve the coding challenges and others who only asked higher-level questions of the AI tool. Some users had the AI tool solve the coding challenges and then retyped the solutions themselves (rather than copying and pasting). This was not an effective strategy.

Generalizing from the coding study

I have spent considerable time both coding and writing and I have always found the processes to have similarities. While others may find this a strange observation, I have always said that coding and writing were the two professional tasks I learned I could not perform later in the evening if I wanted to get a good night’s sleep. Reading was fine. Grading was fine. Something about both coding and writing was cognitively stimulating, making it difficult to sleep.

The application of AI to complex skills is interesting, but difficult to study. Clearly, a single skill would seem very unlikely to be developed if a learner could completely substitute AI for practicing the skill. However, it seems possible that learning a multiple-component skill such as reading or coding might benefit from replacing specific components with AI under certain circumstances. We have limited cognitive capacity and substitution for some components of a complex task could allow the remaining components to receive more attention until well learned.

Learning to write might represent an example. I have often referred to Flower and Hayes’ writing process model when describing the components of writing and writing to learn. The use of AI to offer content to provide the basis for a writing task and perhaps even to offer a structure to guide the organization of a writing product could free up capacity to focus on lower-level skills such as spelling, grammar, and coherent paragraphs. In contrast, I typically use Grammarly while I write to allow to move more quickly while relying on this AI tool to alert me to possible spelling and grammatical improvements.

Part of what Shen and Tamkin observed in their qualitative observations of the different learner-imposed focus of AI and the relationship of differences to what was learned or not learned offers a related perspective. Debugging is an important lower level coding skill and having AI debug code appeared to limit a coder’s ability to debug when working without AI.

Suggestions for Learning to Write and Writing to Learn

AI can support both “learning to write” (developing writing skill) and “writing to learn” (using writing to deepen understanding), but depending on which writing skills are the goal best practices should differ.

Learning to write: skill development

Here AI should be thought of as a coach, not a ghostwriter.

Emphasize feedback: Tools like Grammarly give immediate feedback on grammar, syntax, cohesion, and organization, helping students revise iteratively while concepts are still fresh.

Structure and separate subprocesses: Generative tools can help students brainstorm ideas, outline structures, or identify expectations for different types of writing (e.g., sample introductions, transitions).

Process?first policies: “Writing first, AI second” approaches ask students to draft independently, then use AI for critique and revision. When coders used AI in the Shen and Tamkin, this is the general theme that seemed most successful.

Writing to learn: thinking with text

When the goal is conceptual understanding of content knowledge, AI is best used to amplify reflection, not replace it.

Clarifying concepts for the writer: Students can ask AI to reexplain readings, generate examples, or pose practice questions, then respond in their own words, using writing as a space to consolidate understanding.

Challenge personal understanding: AI can generate counterarguments, alternative explanations, or “what if” scenarios that students must address in writing, pushing them beyond summary toward analysis. Why do others disagree with the summary I am creating? What can I offer to support my position and what are the limitations of the alternative?

Shared design principles

There are some guidelines these goals for writing. Across both purposes, similar design choices matter.

Make process visible: Require artifacts – notes, outlines, draft histories, and brief process memos about when and how AI was used. Document the transition from any use of AI to products student has generated.

Align AI roles with goals: For skills (learning to write), let AI focus on feedback, exemplars, and mechanics; for content learning (writing to learn), keep generative help outside the main composing space and treat it as a prompt.

Previous analysis of technology and the writing process

Sources:

Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition & Communication, 32(4), 365-387

Shen, J. & Tamkin, A. (2026). How AI impacts skill formation. arXiv preprint arXiv:2601.20245 (this study has yet to officially be published)