There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators (n = 318) from a diverse range of teaching levels, experience levels, discipline areas, and regions about the impact of AI on teaching and assessment, the ways that they believe teaching and assessment should change, and the key motivations for changing their practices. The majority of teachers felt that generative AI would have a major or profound impact on teaching and assessment, though a sizeable minority felt it would have a little or no impact. Teaching level, experience, discipline area, region, and gender all significantly influenced perceived impact of generative AI on teaching and assessment. Higher levels of awareness of generative AI predicted higher perceived impact, pointing to the possibility of an ‘ignorance effect’. Thematic analysis revealed the specific curriculum, pedagogy, and assessment changes that teachers feel are needed as a result of generative AI, which centre around learning with AI, higher-order thinking, ethical values, a focus on learning processes and face-to-face relational learning. Teachers were most motivated to change their teaching and assessment practices to increase the performance expectancy of their students and themselves. We conclude by discussing the implications of these findings in a world with increasingly prevalent AI.

Recent data on breakfast consumption among Australian children are limited. This study examined the impact of breakfast skipping and breakfast type (cereal or non-cereal) on nutrient intakes, likelihood of meeting nutrient targets and anthropometric measures. A secondary analysis of two 24-h recall data from the 2007 Australian National Children’s Nutrition and Physical Activity Survey was conducted (2–16 years; n = 4487) to identify (a) breakfast skippers and (b) breakfast consumers, with breakfast consumers further sub-divided into (i) non-cereal and (ii) cereal consumers. Only 4% skipped breakfast and 59% of skippers were 14–16 years. Breakfast consumers had significantly higher intakes of calcium and folate, and significantly lower intakes of total fat than breakfast skippers. Cereal consumers were more likely to meet targets and consume significantly higher fibre, calcium, iron, had significantly higher intakes of folate, total sugars and carbohydrate, and significantly lower intakes of total fat and sodium than non-cereal consumers. The prevalence of overweight was lower among breakfast consumers compared to skippers, and among cereal consumers compared to-cereal consumers (p < 0.001), while no significant differences were observed for mean body mass index (BMI), BMI z-score, waist circumference and physical activity level across the categories. Breakfast and particularly breakfast cereal consumption contributes important nutrients to children’s diets.

There is limited evidence in Australia that compares the nutritional impact of a breakfast cereal breakfast to a non-cereal breakfast, and includes the type of cereal. This study investigated the impact of breakfast choice and the total sugar content of breakfast cereal on nutrient intakes and anthropometric measures among Australian children and adolescents. Data from 2 to 18-year-old in the 2011–2012 National Nutrition and Physical Activity Survey were used (n = 2821). Participants were classified as breakfast cereal consumers (minimally pre-sweetened (MPS) or pre-sweetened (PS)), non-cereal breakfast consumers, or breakfast skippers. Foods consumed for breakfast, foods added to the cereal bowl, and the impact of breakfast choice on daily nutrient intakes and anthropometric measures were determined. Although only 9% of children skipped breakfast, 61% of skippers were aged 14–18 years. Among breakfast consumers, 49% had breakfast cereal, and 62% of these exclusively consumed MPS cereal. Breakfast skippers had a higher saturated fat intake than breakfast cereal consumers, and lower intakes of dietary fibre and most micronutrients (p < 0.001). Compared with non-cereal breakfast consumers, breakfast cereal consumers had similar added and free sugars intakes, lower sodium, and higher total sugars, carbohydrate, dietary fibre, and almost all other micronutrients (p < 0.001). The only difference in nutrient intakes between MPS and PS cereal consumers was higher folate among PS consumers. No associations between anthropometric measures and breakfast or breakfast cereal choice were found. The highest prevalence of breakfast skipping was among 14–18-year old. Breakfast cereal consumers had higher intakes of dietary fibre and most micronutrients compared with non-cereal breakfast consumers and skippers, and almost no differences were found between MPS and PS cereal consumers.

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.

Young women are at an increased risk of micronutrient deficiencies, particularly due to higher micronutrient requirements during childbearing years and multiple food group avoidances. The objective of this study was to investigate biomarkers of particular micronutrients in apparently healthy young women. Female students (n = 308; age range 18–35 year; Body Mass Index 21.5 ± 2.8 kg/m²; mean ± SD) were recruited to participate in a cross-sectional study. Blood samples were obtained from participants in the fasted state and analysed for biomarkers of iron status, vitamin B₁₂, folate, homocysteine, selenium, zinc, and copper. The results show iron deficiency anaemia, unspecified anaemia, and hypoferritinemia in 3%, 7% and 33.9% of participants, respectively. Low vitamin B₁₂ concentrations (0.34 μmol/L. Folate concentrations below the reference range were observed in 1.7% (serum) or 1% (erythrocytes) of participants, and 99.7% of the participant had erythrocyte-folate concentrations >300 nmol/L. Serum zinc concentrations <10.7 μmol/L were observed in 2% of participants. Serum copper and selenium concentrations were below the reference range in 23% and 11% of participants, respectively. Micronutrient deficiencies including iron and vitamin B₁₂, and apparent excess of folate are present in educated Australian female students of childbearing age, including those studying nutrition. The effects of dietary behaviours and food choices on markers of micronutrient status require further investigation.