An analysis of policy documents from 116 R1 U.S. universities found that 63% of these institutions encourage the use of generative AI, with 41% offering detailed guidance for its use in the classroom. More than half of the institutions discussed the ethics of generative AI use, while the majority of guidance focused on using generative AI for writing activities. The research was published in Computers in Human Behavior: Artificial Humans.

Generative AI is a type of artificial intelligence that creates new content such as text, images, audio, code, or video based on patterns learned from large datasets. It works by using models like neural networks to predict and generate outputs that resemble human-created content.

People use generative AI to write documents, summarize information, create artwork, design products, and automate routine tasks. It also supports scientific research by analyzing data, generating hypotheses, and assisting in code or experiment design. Businesses use it for customer support, marketing, prototyping, and improving productivity across many workflows.

In education, generative AI helps students learn by providing explanations, tutoring, and personalized feedback. In medicine, it assists with interpreting data, drafting reports, and even exploring molecular designs for new drugs. Artists and designers use it to explore creative variations and accelerate their creative process. However, generative AI also raises concerns about misinformation, copyright issues, and ethical use.

Study author Nora McDonald and her colleagues wanted to explore what guidance higher education institutions were providing to their constituents about the use of generative AI, what the overall sentiment was regarding its use, and how that sentiment was manifested in actual guidelines.

They were also interested in ethical and privacy considerations, if represented in the guidelines. These authors note that, although the use of generative AI—primarily ChatGPT—became very popular very quickly after its release, there are voices in education that remain staunchly opposed to the use of such applications.

The study authors collected policy documents and guidelines that were publicly available on the internet from 116 R1 institutions, utilizing the Carnegie Classification framework for classifying colleges and universities in the United States. According to this classification, R1 institutions are universities with the highest level of research activity.

The researchers downloaded documents that specifically dealt with generative AI, resulting in a total of 141 documents. Four researchers reviewed 20 of these documents to create a codebook (a coding system for classifying the documents according to their contents). They then used this system to categorize all the other documents.

Results showed that 56% of institutions provided sample syllabi for faculty that included policies on generative AI use, while 55% gave examples of statements regarding usage permissions, such as “embrace,” “limit,” or “prohibit.” Fifty percent provided activities that would help instructors integrate and leverage generative AI in their classrooms, while 44% discouraged the use of detection tools meant to catch AI-generated work. Fifty-four percent provided guidance for designing assignments in ways that discourage the use of generative AI by students, and 23% gave guidance on how to use AI detection tools.

Overall, 63% of universities encouraged the use of generative AI, and 41% offered detailed guidance for its use in the classroom. The majority of guidance focused on writing activities; references to code and STEM-related activities were infrequent and often vague, even when mentioned. Fifty-two percent of institutions discussed the ethics of generative AI regarding a broad range of topics.

“Based on our findings we caution that guidance for faculty can become burdensome as policies suggest or imply substantial revisions to existing pedagogical practices,” the study authors concluded.

The study contributes to the scientific understanding of the stances U.S. universities take on generative AI use. However, the results of the study are based on an analysis of policy documents, not on the study of real classroom practices, which might not fully reflect the provisions specified in the policies.

The paper, “Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,” was authored by Nora McDonald, Aditya Johri, Areej Ali, and Aayushi Hingle Collier.

A study of individuals with autism and their siblings and parents found that autistic individuals and their siblings used fewer causal explanations to connect story elements when asked to tell a story based on a series of pictures. They also used fewer descriptions of the thoughts and feelings of protagonists. The research was published in the Journal of Autism and Developmental Disorders.

Autism is a neurodevelopmental condition characterized by differences in social communication, sensory processing, and patterns of behavior or interests. People on the autism spectrum tend to perceive and organize information in distinctive ways that can be strengths in some contexts and challenges in others. Among other things, they seem to differ from their neurotypical peers in the way they tell stories—specifically regarding their narrative patterns and abilities.

Research shows that many autistic individuals produce narratives that are shorter or less elaborated compared to neurotypical peers, focusing more on concrete details than on social or emotional aspects. Difficulties may appear in organizing stories into a clear beginning, middle, and end, or in emphasizing the motives, thoughts, and feelings of characters. At the same time, many autistic people display strong memory for facts and may provide narratives rich in precise and specific information.

Study author Kritika Nayar and her colleagues wanted to explore and compare the narrative skills of individuals with autism and their first-degree relatives. They wanted to see whether their narrative skills and styles showed similarities with their relatives who do not have autism.

Study participants were 56 autistic individuals, 42 of their siblings who do not have autism, 49 control participants without autism (who were not related to the autistic participants), 161 parents of autistic individuals, and 61 parents who do not have autistic children.

Overall, there were 58 parent-child pairs in the autism group, and 20 parent-child pairs in the control group. The average age of participants with autism and their siblings and peers was approximately 17–19 years. The average age of parents of participants with autism was roughly 50 years, and the average age of parents of non-autistic participants was roughly 46 years.

Study participants were given a 24-page wordless picture book called “Frog, Where Are You?” depicting the adventures of a boy and his dog while searching for a missing pet frog. The story is comprised of five main search episodes in addition to the introduction, plot establishment, and resolution. Participants were asked to narrate the story page-by-page while viewing it on a device that tracked their eye movements.

All audio files of their narration were transcribed and then hand-coded by researchers. Study authors looked for descriptions of affective states and behaviors of protagonists, and protagonists’ cognitive states and behaviors. They also looked for causal explanations of story protagonists’ behaviors and for causal explanations of protagonists’ feelings and cognitions.

The study authors differentiated between explicit causal language, marked by the use of the term “because,” and more subtle use of causal language indicated by words such as “so,” “since,” “as a result,” “in order to,” and “therefore.” They also looked for the presence of excessive detail and for topic perseveration (whether a participant got stuck on a specific topic) throughout the story. Study authors analyzed participants’ eye movements while telling the story.

Results showed that participants with autism and their siblings used fewer descriptions of affect and cognition, and fewer causal explanations than control participants. They were also more likely to omit story components.

Parent groups did not differ in their overall use of causal language or in how often they described feelings and thoughts (cognition) of story protagonists. However, parents of participants with autism used more causal explanations of story protagonists’ thoughts and feelings (affect), but fewer causal descriptions of characters’ behavior compared to control parents. Results also showed some differences in gaze patterns between participants with autism and their siblings on one side, and control participants on the other.

“Findings implicate causal language as a critical narrative skill that is impacted in ASD [autism spectrum disorder] and may be reflective of ASD genetic influence in relatives. Gaze patterns during narration suggest similar attentional mechanisms associated with narrative among ASD families,” study authors concluded.

The study contributes to the scientific understanding of the cognitive characteristics of individuals with autism. However, authors note that the eye-tracking metrics used, which focused on the entirety of the book, might have masked certain important patterns of gaze that could unfold over the course of time.

The paper, “Narrative Ability in Autism and First-Degree Relatives,” was authored by Kritika Nayar, Emily Landau, Gary E. Martin, Cassandra J. Stevens, Jiayin Xing, Sophia Pirog, Janna Guilfoyle, Peter C. Gordon, and Molly Losh.

A biochemical analysis of brains of deceased individuals with Alzheimer’s disease found markers of impaired insulin signaling and impaired mitochondrial function. Analyses also indicated altered neuroinflammation in these brains. The paper was published in Alzheimer’s & Dementia.

Alzheimer’s disease is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior. It is the most common cause of dementia. Alzheimer’s disease typically begins with subtle problems in forming new memories. Over time, the disease disrupts language, reasoning, orientation, and the ability to carry out everyday tasks.

At the biological level, Alzheimer’s is characterized by the accumulation of amyloid-β plaques (abnormal clusters of protein fragments) outside neurons and tau protein tangles (twisted fibers of the tau protein) inside them.

These accumulations make neurons gradually lose their ability to communicate and eventually die, causing widespread brain atrophy. Early symptoms may appear years before diagnosis. There is currently no cure, though some medications and lifestyle interventions might be able to modestly slow symptom progression.

Study author Alex J. T. Yang and his colleagues note that metabolic dysregulation might contribute to the development of Alzheimer’s disease. They conducted a study in which they explored the differences in various metabolic and biochemical indicators between post mortem (after death) brains of individuals who suffered from Alzheimer’s disease and those who did not suffer from dementia. They focused on metabolic signaling, synaptic protein content, morphology of microglia cells in the brain, and markers of inflammation.

These researchers obtained samples from Brodmann area 10 of the brains of 40 individuals from the Douglas Bell Canada Brain Bank (Montreal, Quebec, Canada). Of these individuals, 20 were diagnosed with Alzheimer’s disease, and 20 were not. The number of males and females was equal in both groups (10 men – 10 women). At the time of death, the average age of these individuals ranged between 79 and 82 years, depending on the group.

Study authors used mitochondrial respirometry, Western blotting, cytokine quantification via microfluidic immunoassays, and immunohistochemistry/immunofluorescence to examine metabolic, signaling, and inflammatory markers in the studied brain tissues.

Mitochondrial respirometry is a technique that measures how effectively mitochondria (a type of cell organelle) consume oxygen to produce cellular energy (ATP). Western blotting is a method that separates proteins by size and uses antibodies to detect and quantify specific proteins in a sample.

Cytokine quantification via microfluidic immunoassays is a technique that uses antibodies to measure concentrations of inflammatory signaling molecules. Immunohistochemistry/immunofluorescence is a tissue-staining method that uses antibodies linked to enzymes or fluorescent dyes to visualize the location and amount of specific proteins in cells or tissue sections.

The results showed that brains of individuals with Alzheimer’s disease had markers of impaired insulin signaling and impaired mitochondrial function. They also had greater neuroinflammation. Differences in metabolic signaling markers were higher in female than in male brains, and this dysregulation was worse in women with Alzheimer’s disease.

“This study found that AD [Alzheimer’s disease] brains have distinct metabolic and neuroinflammatory environments compared to controls wherein AD brains present with worse metabolic dysregulation and greater neuroinflammation. Importantly, we also provide evidence that female AD brains are more metabolically dysregulated than males but that female brains may also possess a greater compensatory response to AD progression that likely occurs through a separate mechanism from males,” the study authors concluded.

The study sheds light on biochemical specificities of brains of individuals with Alzheimer’s disease. However, the study was conducted on post mortem human brains. Protein expression in these brains may differ from live ones due to factors such as age, medical history, and the time between death and tissue preservation or analysis.

The paper, “Differences in inflammatory markers, mitochondrial function, and synaptic proteins in male and female Alzheimer’s disease post mortem brains,” was authored by Alex J. T. Yang, Ahmad Mohammad, Robert W. E. Crozier, Lucas Maddalena, Evangelia Tsiani, Adam J. MacNeil, Gaynor E. Spencer, Aleksandar Necakov, Paula Duarte-Guterman, Jeffery Stuart, and Rebecca E. K. MacPherson.

A study involving participants in Canada and the U.S. found that autistic employees are less susceptible to the Dunning–Kruger effect than their non-autistic peers. After completing a cognitive reflection task, autistic participants estimated their own performance in the task more accurately than non-autistic participants. The research was published in Autism Research.

The Dunning–Kruger effect is a cognitive bias in which people with low ability or knowledge in a domain tend to overestimate their competence. This happens because the skills needed to perform well are often the same skills needed to accurately judge one’s performance.

As a result, individuals who lack expertise may also lack the metacognitive insight required to recognize their own mistakes. High-ability individuals, in contrast, may underestimate themselves because they assume tasks that feel easy to them are easy for others.

The effect has been demonstrated in studies where participants with the lowest test scores rated themselves as above average. The bias has been observed in areas such as logical reasoning, grammar, emotional intelligence, and even professional decision-making. It does not mean that all incompetent people are overconfident, but that the tendency to overestimate one’s results is stronger in individuals with lower skill levels.

Study authors Lorne M. Hartman and his colleagues noted that existing evidence indicates that autistic individuals are less susceptible to social influence and cognitive biases than non-autistic individuals. They wanted to explore whether autistic individuals may also be less susceptible to the Dunning–Kruger effect.

These authors conducted a study in which they compared autistic and non-autistic employees’ self-assessments of their performance on a cognitive reflection task. They looked at how much these assessments differed from their objective performance on the task.

Study participants were recruited through autism employment support organizations and social media. In total, the study involved 100 participants. Fifty-three of them were autistic. The average age of autistic participants was 32, and for non-autistic participants, it was 39 years. There were 39 women in the autistic group and 33 women in the non-autistic group.

Participants completed an assessment of autistic traits (the Subthreshold Autistic Trait Questionnaire), allowing study authors to confirm that the autistic group indeed had more pronounced autistic traits than the non-autistic group. They then completed a cognitive reflection test (CRT-Long). This test measures a person’s tendency to override intuitive but incorrect answers and engage in deliberate, analytical reasoning.

After completing this test, participants were asked to estimate how many test questions they answered correctly and to compare their ability to answer those questions to the ability of other people, giving estimates from “I am at the very bottom” to “I am at the very top.”

Results showed that participants who were the least successful in the tasks tended to overestimate their achievement, while those who were the most successful tended to underestimate it. However, the lowest-performing autistic participants overestimated their results significantly less than the lowest-performing non-autistic participants.

When looking at the average (middle) performers, non-autistic participants continued to exhibit greater overestimation of their performance than autistic participants.

Finally, among high-performing participants, autistic individuals underestimated their abilities more than non-autistic participants. While non-autistic high performers slightly underestimated themselves, the autistic high performers demonstrated a stronger tendency to underestimate both their raw scores and their percentile ranking relative to peers.

Overall, the difference between actual and estimated performance was significantly lower for autistic than non-autistic employees.

“Results indicated better calibration of actual versus estimated CRT [cognitive reflection task] performance in autistic employees… Reduced susceptibility to the DKE [Dunning–Kruger effect] highlights potential benefits of autistic employees in the workplace,” the study authors concluded.

The study contributes to the scientific understanding of the cognitive specificities of autistic individuals. However, the authors noted limitations, including a significant age difference between the groups and the fact that the sample consisted almost entirely of employed individuals, meaning the results may not generalize to unemployed autistic adults. Additionally, the study focused on analytical thinking; results may differ in tasks requiring social or emotional intelligence.

The paper, “Reduced Susceptibility to the Dunning–Kruger Effect in Autistic Employees,” was authored by Lorne M. Hartman, Harley Glassman, and Braxton L. Hartman.

A set of studies found that individuals prone to boredom tend to choose faster life history strategies. Similarly, countries with higher boredom proneness scores showed more indicators of faster life history strategies. The research was published in Evolutionary Psychology.

Life history refers to the set of biological and behavioral strategies organisms use to allocate time and energy toward growth, reproduction, parenting, and survival across the lifespan. These strategies include when to mature, how many offspring to have, how much to invest in each offspring, and how long to live.

Life history speed describes where an individual or species falls on a continuum from “fast” to “slow” life strategies. A fast life history involves earlier reproduction, higher risk-taking, shorter planning horizons, and prioritizing immediate rewards. A slow life history involves later reproduction, greater parental investment, long-term planning, and stronger self-regulation.

Humans vary in life history speed depending on ecological conditions, stress, stability, and early-life environments. Unpredictable or harsh conditions tend to push individuals toward faster strategies, favoring earlier and more frequent reproduction. Stable and resource-rich environments tend to promote slower strategies characterized by delayed reproduction and long-term investment.

Study authors Garam Kim and Eunsoo Choi wanted to explore the relationship between boredom and life history strategies (life history speed) at both individual and country levels. They conducted three studies – a pilot study and two additional studies.

The pilot study examined the relationship between boredom proneness and life history strategies among undergraduate students. 97 students participated. 66 of them were women. Their average age was 21.4 years. 79% of them were Koreans.

Participating students completed assessments of boredom proneness (the Boredom Proneness Scale), life history strategies (the Mini-K and the High K Strategy Scale), and impulsive sensation seeking (the Impulsive Sensation-Seeking Scale). Students also reported their monthly household income and rated their perceived family resources.

Study 1 aimed to replicate the results of the pilot study. It was conducted on 298 adults (recruited from an initial pool of 592) through an online panel survey service. Participants completed a survey containing the same assessments of boredom proneness and life history strategies as the pilot study, but also assessments of risk-taking (the Risk-Taking Questionnaire) and future anxiety (the Future Anxiety Scale – Short Form). Future anxiety is the tendency to anticipate future disasters and view the future with dread and uncertainty.

Finally, Study 2 was an analysis of published data aiming to look into associations between boredom proneness and life history strategies on the country level. The study authors hypothesized that people living in boredom-prone countries will be more likely to adopt faster life history strategies.

More specifically, they hypothesized those people would be more open towards casual sex (greater sociosexual unrestrictedness), have shorter lifespans, have more children, give birth earlier in life, and invest less in their children.

Study authors created estimates of boredom proneness, life history strategies, and sexual restrictedness in different countries from published results in various scientific papers. Life expectancy and fertility data came from the UN World Population Prospects 2019. Adolescent birth rates and preprimary school gross enrollment (an indicator of parental investment in children) came from World Bank data and the UNESCO Institute for Statistics data, respectively.

Results of the pilot study confirmed that boredom proneness is associated with a faster life history strategy. Further analysis showed that faster life history strategies mediated the relationship between childhood resources and boredom. In other words, individuals with greater resources as children (whose parents invested more in them) were likely to adopt a slower life history strategy, which in turn made them less prone to boredom.

Results of Study 1 confirmed these results. Boredom proneness was again associated with faster life history strategy. Additionally, individuals with higher boredom proneness were more likely to experience higher future anxiety. Better family resources and socioeconomic status in childhood were associated with lower boredom proneness and slower life history strategies.

The study authors tested a statistical model proposing that worse socioeconomic status in childhood leads to faster life history strategy, which leads to more boredom proneness in adulthood. Results indicated that this chain of relationships is possible.

Finally, on the country level, countries with higher levels of boredom proneness tended to have people more prone to faster life history strategies, specifically regarding shorter lifespans, higher fertility rates, and earlier adolescent birth rates.

“These results suggest that trait boredom may be a functional characteristic of fast life history strategists. This study is the first empirical investigation of trait boredom within a life history framework, highlighting trait boredom’s functional role from evolutionary and ecological perspectives,” study authors concluded.

The study sheds light on the links between boredom proneness and life history strategy. However, it should be noted that the study relied on significant data from self-report questionnaires, leaving room for reporting bias to affect those results. Also, childhood socioeconomic status assessment was based on participants’ recall, introducing the possibility of recall bias.

The paper, “Pace of Life Is Faster for a Bored Person: Exploring the Relationship Between Trait Boredom and Fast Life History Strategy,” was authored by Garam Kim and Eunsoo Choi.