The research team, led by Deborah A. Olarte, PhD, RD, Assistant Professor of Nutrition at New York University, conducted online interviews with 25 elementary school teachers drawn from six elementary schools in Anchorage, Alaska. The study used an observational, qualitative design, asking teachers to reflect on two consecutive school years: the 2021–2022 school year, when the federal universal free school meals policy was in effect, and the following school year, after the policy had ended. This before-and-after structure gave researchers a rare window into how a single policy change rippled through classrooms, cafeterias, and family budgets as perceived by the educators who spend every day with young children.

Universal free school meals emerged during the COVID-19 pandemic as a federal waiver system that allowed schools across the United States to serve breakfast and lunch to all students at no charge, removing the income-based applications and eligibility categories that normally determine who pays and who eats for free. When those waivers lapsed, most districts returned to the traditional tiered system, in which families must qualify for free or reduced-price meals based on household income while other students pay full or reduced rates. The Anchorage teachers in this study experienced that transition directly, and their accounts form the core of the new analysis.

During the year of universal free meals, teachers described a classroom environment noticeably freed from food-related worry. They reported that providing free meals to all students reduced concerns about hunger and eased financial stress for families. Because every child received meals on the same terms, the program also carried the potential to reduce the stigma that can attach to means-tested meal programs, a dynamic the researchers highlight in their conclusions. Not every observation was positive: some teachers perceived that food waste increased while meals were universally free, a concern frequently raised in debates over universal meal policies. Yet when asked what benefits emerged after the policy ended, the teachers could not identify any. Instead, the post-expiration period brought a cluster of problems.

After universal free school meals ended, many teachers observed that fewer students participated in school meals, that more students appeared hungry during the school day, and that some children expressed worry about the cost of meals. Several teachers reported going further than observation: they provided food to students themselves when they noticed children without enough to eat. That detail underscores a quiet reality of American classrooms, in which educators often absorb gaps left by policy, spending their own resources to keep students fed and ready to learn. The study suggests that when the federal safety net narrowed, the burden did not disappear; it shifted onto families and, in some cases, onto teachers.

The anxiety teachers witnessed extended beyond hunger itself to the mechanics of paying for food. Children who once ate without a second thought began expressing concern about meal costs, an emotional burden that educators found visible in the classroom. The researchers frame this as evidence that school meal policies influence more than nutrition. As Olarte explained, teachers witness firsthand how hunger affects students’ ability to learn, focus, and participate in the classroom, and their experiences provide valuable insight into how school meal policies can influence not only nutrition but also student well-being and the overall classroom environment.

The study also captured a dimension of school meals that rarely appears in policy debates: the classroom experience of eating together. Teachers described benefits of eating meals in the classroom, including stronger classroom communities, improved student behavior, and more opportunities to build relationships with students. Shared mealtimes, in this account, functioned as social infrastructure, moments when the boundaries between instruction and care blurred productively. At the same time, the teachers were candid about the costs to themselves. Supervising meals created additional clean-up responsibilities and reduced the planning time they needed for instruction, a trade-off that any future policy design would need to weigh.

The researchers conclude that teacher perspectives should be considered when designing future school meal policies. That recommendation carries weight because teachers occupy a unique observational position: they see children for hours each day, across months, in settings where hunger, distraction, and anxiety are difficult to hide. Survey instruments and cafeteria participation statistics can quantify how many meals were served, but they cannot easily capture a child hesitating at a register, worrying about a balance owed, or quietly declining a meal. The qualitative method used here, built on in-depth interviews rather than standardized questionnaires, is designed to surface exactly those lived experiences and to translate them into evidence that policymakers can act upon.

The findings arrive at a moment when the role of school meals in child well-being is under active discussion across the United States. Some states have moved to adopt their own universal free school meal programs after the federal waivers ended, while other districts continue to operate under the traditional income-based eligibility model. The Anchorage study offers a grounded case study of what the rollback looked like from the front of the classroom: increased hunger, increased anxiety about meal costs, and decreased participation in the very programs designed to feed children. The authors suggest that universal free school meals may help improve student well-being, reduce stigma surrounding school meals, and support a more positive learning environment, positioning the policy not merely as a nutrition program but as an educational intervention with consequences visible in attention, behavior, and classroom community.

For the teachers at the center of the study, the contrast between the two school years was stark enough to reshape how they think about food in schools. During universal free meals, they saw fewer hungry children and calmer families; after the policy ended, they saw the reverse, and some began feeding students out of their own supplies. The study, titled When School Meals Are No Longer Free: A Qualitative Exploration of Elementary School Teachers’ Experiences and Perceptions, does not claim to measure outcomes across an entire district or to establish causation with statistical precision. What it does provide is a detailed, systematic record of educator perceptions during a natural policy experiment, gathered from 25 teachers across six schools and published in a journal that reaches the nutrition education professionals who shape school food programs. As states and districts weigh whether to extend or restore universal meal access, the message from these classrooms is that the price of a school lunch is visible not only on a family’s bill but in a child’s ability to focus, participate, and feel secure during the school day.

Subject of Research: Teacher-reported effects of the expiration of the federal universal free school meals policy on elementary students in Anchorage, Alaska

Article Title: Elementary school teachers report increased student hunger and anxiety after universal free school meals ended

Article References: Elementary school teachers report increased student hunger and anxiety after universal free school meals ended. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: universal free school meals, school nutrition, child hunger, elementary education, Anchorage Alaska, Journal of Nutrition Education and Behavior, food insecurity, student well-being, classroom behavior, school meal participation, qualitative research, education policy

The original HelexKids, released in 2017, was the first psycholinguistic database built specifically for Greek primary school children aged six to twelve. It compiled frequency information from 76 textbooks spanning six grade levels and subjects ranging from language arts to mathematics, science, and history. That earlier version revealed striking facts about the vocabulary children encounter: roughly half of the words at each grade level appeared only once in the entire corpus, words were shorter and more frequent than in adult Greek databases, and the top 100 words alone accounted for nearly 45 percent of all tokens. What it lacked, however, was grammatical and phonological depth, a gap that limited its usefulness for studies of reading development, speech and spelling interventions, and theoretical work on how sound and grammar interact.

HelexKids 2.0 closes that gap with a rich annotation scheme. Every entry now carries a part-of-speech tag, syllable count, a full phonetic transcription in the International Phonetic Alphabet, orthographic and phonological syllabification, word-level syllable templates, and detailed stress pattern information. Nouns receive additional morphological annotation covering case, number, gender, and inflection class. The database also retains all original frequency measures, including raw counts, Zipf values, orthographic Levenshtein distance, dispersion, and contextual diversity, while adding new metrics such as phonological Levenshtein distance and bigram and biphone frequencies.

Building the annotations was far from a purely mechanical exercise. The team first corrected systematic character-encoding errors introduced when scanned textbooks were converted to spreadsheets, in which Latin letters had been substituted for visually similar Greek characters. Initial part-of-speech tagging drew on two existing annotated resources, GreekLex 2 and the A-Clean database, but roughly 45,000 words, about two-thirds of the total, remained untagged. The researchers then tested spaCy’s machine learning models for Greek, which significantly underperformed: the word for ‘bag’ was tagged as a proper noun, and ‘despair’ was labeled a verb. Ambiguity posed a deeper problem, since the textbook words came without sentence context. A form like ‘διορθώσεις’ can be either the plural noun ‘corrections’ or the verb ‘you correct’, and words such as ‘ένα’ can serve as a numeral, an article, or a pronoun depending on context.

Faced with these challenges, the team chose to manually annotate the entire lexicon using linguistically motivated criteria drawn from authoritative Greek grammars and dictionaries. Where genuine ambiguity existed, they assigned compound tags such as ADV/NOUN or PCP/ADJ/NOUN to capture a word’s multifunctionality. Quality control was rigorous: three researchers independently annotated a random sample of 1,000 words, yielding a Krippendorff’s alpha of 0.789 for part-of-speech tagging. After refining the guidelines, the final scheme contained 61 distinct categories and category combinations. Morphological annotation of nouns achieved even stronger agreement, with alpha reaching 0.827.

The phonological layer demanded equally careful engineering. Custom R scripts automated syllabification, transcription, and stress assignment, but Greek’s notorious variability required human oversight at every step. Vowel sequences such as ‘ια’ may be realized as a single syllable with a palatal consonant, as in the word for ‘eyes’, or split across two syllables, as in some pronunciations of ‘I delete’. Monosyllabic content words like ‘earth’ carry stress without written accents, capitalized words lose their accent marks entirely, and clitic constructions produce double stress. Approximately 3.2 percent of word types received dual annotations to reflect legitimate pronunciation variation, a design choice the authors say acknowledges the gradient nature of phonological output.

The redesigned database is also a technical leap. Implemented as a relational MariaDB database with nine interconnected tables, HelexKids 2.0 assembles lexicons dynamically on demand rather than storing fixed lists. Users can query by grade level or cumulative grade range, part of speech, syllable count, stress pattern, frequency thresholds, neighborhood density, and phonotactic measures simultaneously, through a bilingual English-Greek web interface hosted on the GRADIENCE project webpage. Results export cleanly to CSV or Excel with proper handling of Greek and IPA characters.

The descriptive statistics already yield insights into how children’s linguistic input evolves across schooling. Vocabulary expands unevenly: the largest jump occurs between Grades 2 and 3, with types growing nearly 88 percent and tokens nearly 140 percent, coinciding with the introduction of more school subjects, while growth slows to about 7 percent for types between Grades 5 and 6. Words also lengthen as grades advance. Four-syllable and longer words rise from 35.1 percent of types in Grade 1 to 53.4 percent in Grade 6, and average word length grows from 3.25 to 3.75 syllables. The most common word template across all grades is a three-syllable, penultimately stressed word made entirely of open consonant-vowel syllables, though token-based analysis shows that short monosyllabic function words dominate actual reading volume.

Perhaps the most theoretically consequential findings concern stress. Greek is a morphology-dependent stress system in which accent can fall on any of the final three syllables and cannot be predicted from sound structure alone. The database shows that each major word class distributes stress differently: nouns favor penultimate stress, verbs show the highest proportion of antepenultimate stress among their types yet their most frequent token forms are penultimately stressed, adjectives overwhelmingly take final stress, and adverbs prefer penultimate stress. For nouns, penultimate stress gradually declines across grades while antepenultimate stress rises, a redistribution that leaves final stress untouched. The authors argue these patterns support accounts distinguishing morpholexically conditioned stress from phonological defaults, and reinforce proposals that nouns are prosodically more complex than verbs.

The implications extend well beyond Greek linguistics. HelexKids 2.0 joins a small international family of child-specific databases, including MANULEX for French, childLex for German, ESCOLEX for Portuguese, and CYP-LEX for English, but its phonological depth exceeds them all, offering a template for annotation projects in other languages. For educators, the resource promises practical tools: stress position is a documented difficulty for Greek children in both spelling and reading, and teachers can now generate targeted word lists organized by stress pattern, starting with high-frequency, predictable items before moving to rarer, unpredictable ones. The team acknowledges limitations, including the restriction of morphological annotation to nouns and the database’s reliance on textbooks from a specific 2007 to 2013 period, with new textbooks scheduled from 2027 onward. Thanks to its expandable architecture, the annotation pipeline can absorb new materials with minimal modification, positioning HelexKids 2.0 as a living resource for developmental psycholinguistics, morphophonological theory, and evidence-based literacy instruction.

Subject of Research: A linguistically annotated Greek child lexical database with part-of-speech, phonological, and morphological annotations

Article Title: HelexKids 2.0: A linguistically annotated lexical database building on HelexKids

Article References: Revithiadou, A., Terzopoulos, A., Niolaki, G., Markopoulos, G., Avdelidis, K., Mittas, I., & Kosmidis, K. (2026). HelexKids 2.0: A linguistically annotated lexical database building on HelexKids. Behavior Research Methods, 58(11), Article 311. https://doi.org/10.3758/s13428-026-03179-7

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03179-7

Keywords: lexical database, Greek language, word frequency, psycholinguistics, stress patterns, morphophonology, elementary education, reading development, phonetic transcription, part-of-speech tagging, textbook vocabulary, open access resource

The research team, led by Jiawei Shao of the Centre for Instructional Technology and Multimedia at Universiti Sains Malaysia, together with Ju Jinming and Shupeng Tang, recruited 174 Chinese fifth-grade students. Using individual-level random assignment, the researchers divided the children into three groups. One group learned with the full AI-supported Gamified Learning Program, which the authors abbreviate as AI-GILP. A second group used a gamified learning program without AI support, known as GILP, while a third received traditional lecture-based instruction. The design is notable for its rigor: rather than comparing a new technology against nothing, the study isolated the specific contribution of the AI layer by holding the gamification constant across two of the three conditions.

Mathematics anxiety, learning motivation, and achievement were measured at three points: before the intervention, immediately after the two-week program, and again four weeks later at a delayed posttest. The researchers analyzed the data using analysis of covariance and mixed-design analyses of variance, standard statistical techniques for detecting group differences while accounting for baseline performance. The results were striking on the emotional and motivational front. The AI-supported condition produced significantly lower mathematics anxiety and significantly higher learning motivation than both the gamified-only condition and traditional instruction, with overall group effects corresponding to partial eta-squared values of .208 for anxiety and .184 for motivation, both of which represent substantial effects in educational research.

The achievement results told a more nuanced story. Both gamified conditions outperformed traditional lecture-based instruction on mathematics achievement, confirming that gamification itself carries real academic benefits. However, the AI-supported group did not significantly outperform the gamified-only group on achievement, with a Cohen’s d of 0.19, a small difference that did not reach statistical significance. In other words, the AI companion appeared to work primarily on how children felt about mathematics rather than on how much mathematics they learned, at least over the timescale of this study. At the delayed posttest, the descriptive means for anxiety and motivation retained the same favorable ordering for the AI-supported group, although the authors note that pairwise significance at that assessment was not formally tested.

Perhaps the most revealing detail lies in where the motivational advantage came from. The researchers found that the AI-supported program’s motivational benefit was concentrated in intrinsic motivation and perceived competence, two dimensions closely tied to whether learners engage with a subject because they genuinely enjoy it and believe they can succeed. Notably, there was no significant difference in the effort/importance dimension. This pattern suggests that the AI companion did not simply push children to work harder; instead, it appears to have nurtured a sense of capability and genuine interest, which many learning scientists consider more durable drivers of long-term engagement.

The technology behind these effects is documented in unusual detail in the study’s appendix, and it is the engineering that makes the findings credible. The AI Learning Companion, as the system is called, is built on a modular workflow architecture with four functional modules: a dialogue module, a hint-hierarchy module, a behavior-triggered support module, and a personality-configuration module. When students watch recorded instructional videos or complete gamified mathematics tasks, they can activate the companion through a robot icon and ask questions by speech or text. An Intent Classifier categorizes each input as conceptual, procedural, help-seeking, or another predefined interaction type, and a Knowledge Router retrieves relevant concepts, common misconceptions, and illustrative examples from an embedded knowledge base.

The hint system follows a deliberately escalating structure based on a clue-reasoning-example sequence. At the first level, clue-based prompts direct students’ attention to key information and activate prior knowledge without giving anything away. If a learner makes three consecutive errors on the same task or remains inactive for a predefined period, the system advances to the second level, where reasoning-oriented prompts identify the relevant mathematical relationships and guide the student toward the required problem-solving procedure. Only at the final level does the system present a worked example or visual demonstration. This design gives children the chance to attempt tasks independently before receiving the most explicit assistance, a structure informed by Self-Determination Theory’s principles of competence support and learner choice.

Crucially, the system does not claim to read minds. The Behavior-Triggered Support Module does not measure students’ emotional states or diagnose anxiety and frustration. Instead, it monitors observable interaction indicators such as error frequency, time-on-task, repeated inactivity, and the frequency of hint requests. When predefined combinations of these indicators are reached, the companion offers supportive messages through voice or text, with examples including encouragement to try again together or reminders of what the learner has already mastered. The system also delivers gamified rewards, adding points and displaying badges after correct responses, and it allows students to choose among three interaction personalities: scholarly, humorous, or adventurous, each with a different communication tone that children can switch at will.

The authors are refreshingly careful about what their study does and does not demonstrate. The proposed mechanisms involving psychological-need support, emotional regulation, adaptive feedback, and motivational internalization were not directly tested, and the study did not isolate the effects of individual companion modules or measure their mediating processes. The supportive messages should not be interpreted as evidence that the system detected a learner’s emotional state or that children perceived the messages as empathic. What the experiment does establish is that the complete AI-supported package, compared against both a well-matched gamified control and traditional teaching, produced meaningful reductions in anxiety and gains in specific motivational dimensions, while the achievement gains were attributable primarily to gamification itself.

For educators and developers watching the rapid arrival of AI tutors in classrooms, the study offers a valuable calibration. The biggest wins from adding an AI layer to gamified learning may not show up immediately in test scores but in the emotional climate of the mathematics classroom, where anxiety quietly erodes participation and confidence. If future process-oriented research confirms that adaptive feedback, tiered hints, and configurable companionship are the active ingredients, the blueprint documented here, from intent classification to escalating hints to behavior-triggered encouragement, could become a reference architecture for learning technologies that treat children’s feelings as seriously as their answers.

Subject of Research: Effects of an AI-supported gamified learning program on elementary students' mathematics anxiety, motivation, and achievement

Article Title: The effects of an AI-supported gamified learning program on elementary students' mathematics anxiety, learning motivation, and achievement

Article References: Shao, J., Jinming, J., & Tang, S. (2026). The effects of an AI-supported gamified learning program on elementary students' mathematics anxiety, learning motivation, and achievement. BMC Psychology. https://doi.org/10.1186/s40359-026-05367-8

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05367-8

Keywords: artificial intelligence, gamified learning, mathematics anxiety, learning motivation, elementary education, randomized controlled trial, Self-Determination Theory, educational technology, mathematics achievement, AI learning companion, intrinsic motivation, BMC Psychology

The research team, led by Deepika Menon of the University of Nebraska-Lincoln together with colleagues at Southern Methodist University, Towson University, and Indiana University Southeast, used a mixed-methods design combining a validated self-efficacy survey with open-ended questionnaires administered at the beginning and end of science or STEM methods courses. The survey instrument, known as SETIS, measures three dimensions of confidence: personal capability, the ability to connect disciplines and engage students, and the capacity to manage materials and technology. Statistical analyses, including repeated-measures multivariate tests and paired comparisons, revealed significant gains across all three dimensions by semester’s end, with the largest improvement in teachers’ beliefs about their own abilities.

That growth matters because self-efficacy, a concept rooted in social cognitive theory, is strongly linked to whether teachers adopt innovative practices, persist through difficulties, and remain in the profession. Teachers with low confidence in STEM tend to avoid reform-based instruction and give up more easily when lessons falter. The finding that a single semester of hands-on, inquiry-based methods coursework, whether embedded in a science methods course or a dedicated STEM semester, produced medium-to-large gains suggests that teacher preparation programs can move the needle on the beliefs that ultimately shape classroom practice.

Yet the qualitative side of the study tells a more complicated story. Analyzing roughly 1,300 coded response segments, the researchers identified six broad categories of perceived challenges: teacher affect and experience, student-related concerns, content and curricular demands, pedagogical difficulties, support from colleagues and parents, and time and resources. By the end of the semester, participants reported significantly fewer overall challenges, particularly around science content knowledge and pedagogical confidence, but two concerns intensified rather than faded: worries about teaching engineering and doubts about integrating technology.

The researchers interpret this pattern through two complementary frameworks. Windschitl’s model of teacher dilemmas, spanning conceptual, pedagogical, cultural, and political tensions, helps explain how future teachers wrestle with what integrated STEM knowledge even means and how to enact it in real classrooms. Ertmer’s distinction between first-order barriers, such as limited time, materials, and institutional support, and second-order barriers rooted in personal beliefs and confidence, maps closely onto the challenges participants described. Content knowledge gaps and pedagogical uncertainty, the internal barriers, shrank after coursework. External constraints, especially time for planning and teaching STEM amid crowded elementary schedules, grew more salient as participants gained a clearer-eyed view of classroom realities.

Several findings stand out for their implications. Concerns about meeting state standards and assessment demands barely budged, with participants observing that elementary curricula leave little room for STEM when reading and mathematics dominate. One participant at a STEM integration school noted that despite the label, STEM instruction was rare. Meanwhile, structural equation modeling revealed that teachers who still felt unprepared in science or STEM content at semester’s end scored significantly lower on self-efficacy, confirming that content preparedness remains a critical lever. Intriguingly, identifying technology access as a challenge was associated with higher self-efficacy, suggesting that more confident teachers may simply be more aware of the resource constraints they will face.

The study also surfaced demographic patterns that warrant attention. Participants who had spent five or more years in college reported lower self-efficacy, possibly because their extended, full-year student teaching exposed them to a more sobering view of classroom challenges. Hispanic/Latino participants reported lower self-efficacy than their White peers, a result the authors connect to broader literature on inequities in STEM access and representation, and one they argue demands further investigation across more diverse populations.

The authors are careful about limits. Because data were self-reported at only two time points, and because most participants had not yet completed full-time student teaching, self-efficacy estimates may be somewhat inflated. The analysis also pooled data across five quite different programs, so it cannot isolate which specific course features drove the gains. Still, the breadth of the sample strengthens confidence that the overall pattern, rising efficacy alongside persistent and evolving concerns, reflects something real about how teacher preparation works.

The practical implications are pointed. The researchers recommend that preparation programs increase dedicated attention to engineering design and technology integration, precisely the areas where confidence lagged or declined. They suggest strategic school and informal-education placements so future teachers can witness successful integrated STEM instruction in action, something many mentor elementary classrooms rarely model. They also call for partnerships between universities and school administrators to address resource gaps, and for collaboration between STEM content faculty and education faculty to build better curricula. Longitudinal follow-up studies, the authors argue, should track whether the challenges these future teachers anticipate actually materialize once they enter elementary classrooms, closing the loop between preparation and practice.

Subject of Research: Preservice elementary teachers' integrated STEM teaching self-efficacy and perceived challenges

Article Title: “I learned a lot, but…”: preservice elementary teachers’ integrated STEM teaching self-efficacy and perceived challenges

Article References: Menon, D., Wieselmann, J. R., Haines, S., Asim, S., & Johnson, A. (2026). “I learned a lot, but…”: preservice elementary teachers’ integrated STEM teaching self-efficacy and perceived challenges. International Journal of STEM Education, 13(1), Article 56. https://doi.org/10.1186/s40594-026-00645-8

Image Credits: AI Generated

DOI: 10.1186/s40594-026-00645-8

Keywords: integrated STEM, self-efficacy, preservice teachers, elementary education, teacher preparation, STEM challenges, engineering education, methods coursework, mixed methods, structural equation modeling, educational psychology, K-12 STEM