Mahindra University

Numerous fruitful experiments and studies have been conducted to show the effectiveness of technology integration in second and foreign language learning. Researchers have suggested using online tasks in routine classroom practice and urged teachers to blend digital tasks with traditional teaching tasks as they have been proven beneficial in many ways. This paper aims to explore the utility of online collaborative writing, a process where adult ESL learners at the tertiary level work together to create and discuss written content using digital platforms, such as pre-speaking tasks, and their effects on learners’ group discussion task preparation and performance. These pre-tasks were introduced as a prelude to face-to-face group discussion tasks to familiarize learners with the topic and build their confidence. To understand the effectiveness of these pre-tasks, learners’ performance during pre-tasks and tasks was analysed. Further, learners’ reflective journals and semi-structured interview scripts were analysed to determine their perceptions about the pre-tasks. The findings of the study suggest, online collaborative writing pre-tasks favour group discussion tasks by lowering learners' apprehension about participating in face-to-face dialogues and boosting task familiarity. Moreover, the pre-tasks develop learners' linguistic and content-related schema, which motivates learners to participate in complex tasks like group discussions. The study advises adopting online collaborative writing pre-tasks as a warm-up for effective group discussion task performance. This study leads to the possibility of conducting future research to discover the specific linguistic features that get transferred from online to face-to-face communication and its order of acquisition among young ESL learners.

Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computational cost. MLPs approximate the underlying potential energy surface, enabling efficient computation of energies and forces in solvated systems. They are also capable of accounting for effects such as hydrogen bonding, long-range polarization, and conformational changes. This review surveys the development and application of MLPs in solvation modeling. We summarize the theoretical basis of MLP-based energy and force predictions and present a classification of MLPs based on training targets, model types, and design choices related to architectures, descriptors, and training protocols. Integration into established solvation workflows is discussed, with case studies spanning small molecules, interfaces, and reactive systems. We conclude by outlining open challenges and future directions toward transferable, robust, and physically grounded MLPs for solvation-aware atomistic modeling.