School closures due to teacher strikes or political unrest in low-resource contexts can adversely affect children’s educational outcomes and career opportunities. Phone-based educational technologies could help bridge these gaps in formal schooling, but it is unclear whether or how children and their families will use such systems during periods of disruption. We investigate two mobile learning technologies deployed in sub-Saharan Africa: a text-message-based application with lessons and quizzes adhering to the national curriculum in Kenya (N = 1.3 million), and a voice-based platform for supporting early literacy in Côte d’Ivoire (N = 236). We examine the usage and beliefs surrounding unexpected school closures in each context via system log data and interviews with families about their motivations and methods for learning during the disruption. We find that mobile learning is used as a supplement for formal and informal schooling during disruptions with equivalent or higher intensity, as parents feel responsible to ensure continuity in schooling.

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Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance-and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance-and document-level syntactic similarity which pairs and averages the most similar dependency parse trees between a pair of documents based on tree kernels. FastKAS-SIM is more robust to syntactic dissimilarities and differences in length, and runs up to to 5.2 times faster than our baseline method over the documents in the r/ChangeMyView corpus. * denotes equal contribution.Utterance 1: When we hate, we always move away from the grace of God. When we become resentful and unforgiving, the world around us seems spiteful and meaningless. Utterance 2: How can you be skiing if you are already swimming? FastKASSIM Score: 0.219 CASSIM Score: 0.838 LSM Score: 0.623 Utterance 1: I like swimming because it is cool. Utterance 2: I love running because it is fun.

Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as incontext examples to synthesize a social conversation dataset using prompting 1 . We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multiparty conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset.

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