This study examines how pre-service teachers in early childhood education (ECE) and elementary education (ELE) integrate ChatGPT into mathematics lesson planning. Using a mixed-methods case study framed by the Technological Pedagogical Content Knowledge (TPACK) with Contextual Knowledge (XK) framework and AI literacy, we analyzed the experiences of 44 pre-service teachers (24 ECE, 20 ELE) in mathematics method courses at a university in the Midwestern United States. Data sources included pre- and post-assessments, documentation of ChatGPT interactions, lesson plans, and reflection papers. Findings reveal significant gains in participants' knowledge of and confidence in using ChatGPT, with increased willingness to incorporate generative AI tools into their teaching practices. Thematic analysis identified six categories of ChatGPT use in lesson planning, from content generation to final refinement, while highlighting challenges in adapting AI-generated content to meet developmentally appropriate practices and specific classroom needs. While both ECE and ELE groups followed similar patterns in ChatGPT usage, their approaches varied based on grade-level contexts. This study demonstrates how the development of integrated TPACK-XK competencies and AI literacy supports effective ChatGPT integration in math lesson planning. Findings contribute to understanding how teacher education programs can prepare future educators to integrate generative AI technologies while maintaining pedagogical integrity.
The Process Oriented Guided Inquiry Learning for Physical Chemistry Laboratory (POGIL-PCL) community has developed guided-inquiry experiments for physical chemistry laboratory courses with the goal of improving student engagement and meaningful learning. The present study evaluated and compared the extent to which four POGIL-PCL experiments and four analogous traditional experiments support student engagement in scientific practices. To account for the specific context of POGIL-PCL, we developed a modified three-level version of the Three-Dimensional Learning Assessment Protocol that considers the role of instructor facilitation and the context of advanced students who have become integrated into their communities of practice. In comparison to their traditional counterparts, the POGIL-PCL experiments were found to offer more consistent and higher-level opportunities for student engagement in scientific practices. The POGIL-PCL experiments showed stronger alignment with some practices more than others, namely, analyzing and interpreting data, using mathematics and computational thinking, developing and using models, and planning and carrying out investigations. The cyclic nature of POGIL-PCL provided students with repeated and iterative opportunities for engagement with scientific practices within a given experiment, and it was found that those opportunities for engagement in certain scientific practices were concentrated in particular parts of the POGIL-PCL data-think cycle. These findings provide insight into how POGIL-PCL experiments can be strengthened by the incorporation of prompts that more deliberately cue for additional scientific practices as well as through revisions to make engagement with the practices more consistent across a variety of contexts.
Swarm robotics enables groups of simple robots to collaboratively perform complex tasks through local interactions and self-organisation. While centralised and rulebased swarm systems have shown promising results, they suffer from scalability limitations, single-point failures, and poor adaptability in dynamic environments. This paper presents a fully decentralised swarm robotics framework that integrates artificial intelligence-driven decision making using multi-agent reinforcement learning. Each robot operates as an autonomous agent relying solely on local observations and limited peer-to-peer communication, eliminating the need for global state information or centralised control. The proposed framework employs Independent Proximal Policy Optimization (IPPO) for decentralized policy learning under partial observability. Extensive simulation-based evaluation demonstrates that the proposed approach significantly outperforms rule-based and centralised baselines, achieving up to a $38 \%$ reduction in task completion time and a $25 \%$ improvement in fault tolerance under robot failure scenarios. Experiments were conducted using the PyBullet simulator with 1000 training episodes and local peer-to-peer communication constraints. The results confirm that decentralised learning-based swarm intelligence provides a scalable, resilient, and efficient solution for autonomous multi-robot coordination in complex environments.
Phosphorus (P) availability is often limited in subtropical acidic soils due to fixation by iron and aluminum oxides, constraining nutrient uptake and productivity in Camellia oleifera plantations. However, the mechanisms by which the effects of artificial nitrogen (N) application and natural N fixation via legume intercropping on soil P dynamics remain poorly understood. In this study, the independent effects of legume intercropping and N application on soil P fractions, soil biochemical properties and leaf nutrient content were investigated in C. oleifera plantations in subtropical China. Six treatments were applied: monoculture with weeding, monoculture without weeding, intercropping with Cassia tora or peanut, and monoculture with low or high N application (25 or 50 g urea per plant). Soil P fractions, soil organic carbon, total N, pH, ammonium (NH4+-N), nitrate (NO3--N), acid and alkaline phosphatase activities, and leaf C, N, and P contents were measured at the growth (July) and mature (September) stages. Results showed that both legume intercropping and low N application independently enhanced total and labile soil P, increased soil organic carbon, and improved leaf nutrient contents compared to the control. High N initially reduced labile P but partially recovered by maturity. Phosphatase activities declined at maturity but remained higher in intercropped and fertilized plots, indicating improved P cycling. Nitrate N concentrations increased from the growth stage to the mature stage. These results suggest that legume intercropping and N application, when applied independently, each promote soil P availability and plant nutrient uptake, highlighting practical strategies to enhance soil fertility and sustain C. oleifera production in subtropical acidic soils.
