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Abstract We provide our perspective on$${\mathbb{X}}$$ $X$ -Learning ($${\mathbb{X}}$$ $X$ L), a novel distributed learning architecture that generalizes and extends the concept ofdecentralization. Our goal is to present a vision for$${\mathbb{X}}$$ $X$ L, introducing its unexplored design considerations and degrees of freedom. To this end, we shed light on the intuitive yet non-trivial connections between$${\mathbb{X}}$$ $X$ L, graph theory, and Markov chains. We also present a series of open research directions to stimulate further research. 

Multimodal Multitask Federated Foundation Models (M3T-FedFMs) represent a new frontier in artificial intelligence (AI), enabling integration of diverse data modalities and multitask learning while preserving data confidentiality through federated learning. Although still in their infancy, these models hold immense promise for advancing psychiatric research, particularly in the characterization and assessment of mood disorders. In this perspective paper, we articulate a forward-looking vision for deploying M3T-FedFMs in psychiatric practice and delineate key challenges and open research directions critical for realizing next-generation, AI-driven mental health care. 

Abstract: Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting interactions. Accurately predicting future interactions in these networks (i.e., link prediction) is crucial for enabling effective collaborative learning, supporting timely instructional interventions, and informing the design of effective group-based learning activities. However, traditional link prediction approaches are typically tuned to general online social networks (OSNs), often overlooking the complex, non-Euclidean, and dynamically evolving structure of SLNs, thus limiting their effectiveness in educational settings. In this work, we propose a graph neural network (GNN) framework that jointly considers the temporal evolution within classrooms and spatial aggregation across classrooms to perform link prediction in SLNs. Specifically, we analyze link prediction performance of GNNs over the SLNs of four distinct classrooms across their (i) temporal evolutions (varying time instances), (ii) spatial aggregations (joint SLN analysis), and (iii) varying spatial aggregations at varying temporal evolutions throughout the course. Our results indicate statistically significant performance improvements in the prediction of future links as the courses progress temporally. Aggregating SLNs from multiple classrooms generally enhances model performance as well, especially in sparser datasets. Moreover, we find that jointly leveraging both the temporal evolution and spatial aggregation of SLNs significantly outperforms conventional baseline approaches that analyze classrooms in isolation. Our findings demonstrate the efficacy of educationally meaningful link predictions, with direct implications for early-course decision-making and scalable learning analytics in and across classroom settings. 

Abstract: Learning to program involves repeated trial-and-error attempts, leading to substantial heterogeneity in learning behavior across student cohorts and course offerings. Code-aware knowledge tracing (KT) models aim to capture how students' programming knowledge evolves over time. However, most existing KT studies rely on data from individual courses, where student interactions are often sparse and insufficient for modeling robust performance patterns. Centralizing student data across classrooms or institutions to address this limitation is often constrained by the risk of privacy breach of sensitive code submission data, limiting the impact of existing KT methods. Federated learning (FL) provides a privacy-preserving alternative by enabling collaborative model training without sharing raw data, and recent work has shown that model personalization can further improve federated KT performance. However, the interaction between model personalization and code representation has not been systematically studied. In this paper, we present a comparative analysis of code-aware KT representations under personalized federated learning (pFL) framework, using two semesters of CodeWorkout data treated as separate clients. Our results show that personalization consistently improves predictive accuracy under federated training, but the magnitude and consistency of these improvements depend on how student code is represented. In particular, code representations that capture richer structural information and syntax-aware encodings exhibit more reliable performance gains and promise more stable learning trajectories across assignments and semesters, especially for more complex programming tasks. These findings provide practical insights for EDM researchers and practitioners on how representation choice shapes the effectiveness of personalization when deploying privacy-preserving knowledge tracing models in programming education. 

Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving training across decentralized institutions while accommodating diverse modalities and tasks. Subsequently, this perspective paper aims to unveil M3T FedFMs as a promising yet underexplored approach to the education community, explore its potentials, and reveal its related future research directions. We outline how M3T FedFMs can advance three critical pillars of next-generation intelligent education systems:(i) privacy preservation, by keeping sensitive multi-modal student and institutional data local;(ii) personalization, through modular architectures enabling tailored models for students, instructors, and institutions; and(iii) equity and inclusivity, by facilitating participation from underrepresented and resource-constrained entities. We finally identify various open research challenges, including studying of (i) inter-institution heterogeneous privacy regulations, (ii) the non-uniformity of data modalities' characteristics, (iii) the unlearning approaches for M3T FedFMs, (iv) the continual learning frameworks for M3T FedFMs, and (v) M3T FedFM model interpretability, which must be collectively addressed for practical deployment. 

Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garnered attention, most existing approaches rely on centralized training, where data is aggregated and processed on a local/cloud server with direct access to raw data. However, in real-world educational settings, such direct access across multiple classrooms is often restricted due to privacy concerns. Furthermore, training models on isolated classroom data prevents the identification of common interaction patterns that exist across multiple classrooms, thereby limiting model performance. To address these challenges, we propose one of the first frameworks that integrates Federated Learning (FL), a distributed and collaborative machine learning (ML) paradigm, with SLNs derived from students' interactions in multiple classrooms' online forums to predict future link formations (i.e., interactions) among students. By leveraging FL, our approach enables collaborative model training across multiple classrooms while preserving data privacy, as it eliminates the need for raw data centralization. Recognizing that each classroom may exhibit unique student interaction dynamics, we further employ model personalization techniques to adapt the FL model to individual classroom characteristics. Our results demonstrate the effectiveness of our approach in capturing both shared and classroom-specific representations of student interactions in SLNs. Additionally, we utilize explainable AI (XAI) techniques to interpret model predictions, identifying key factors that influence link formation across different classrooms. These insights unveil the drivers of social learning interactions within a privacy-preserving, collaborative, and distributed ML framework—an aspect that has not been explored before.