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Abstract Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula. 

Abstract Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications.