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Spontaneous epimutations—stochastic changes in cytosine methylation—can persist across generations in plants and are thought to contribute to phenotypic variation. Although epimutations are increasingly studied for their potential long-term effects, it remains unclear why their accumulation varies across genotypes. Here, we tracked DNA methylation across ten generations in ~400 mutation accumulation lineages derived from ~70ArabidopsisLer × Cvi recombinant inbred lines. Treating epimutation rates as quantitative molecular traits, we mapped a major QTL to a Cvi-derived deletion nearVIM2andVIM4, two genes involved in CG methylation (mCG) maintenance. We show that this deletion rapidly reduces genome-wide methylation to a lower steady-state and compromises mCG maintenance fidelity across generations, resulting in a ~1.5-fold increase in epimutation rates. Genotypes with elevated rates exhibited accelerated epigenetic drift and phenotypic divergence. Our findings support a punctuated-equilibrium model of mCG evolution, in which sudden disruptions to methylation homeostasis can destabilize epigenetic inheritance over longer time-scales. 

Errors in AI grading and feedback are by their nature non-deterministic and difficult to completely avoid. Since inaccurate feedback potentially harms learning, there is a need for designs and workflows that mitigate these harms. To better understand the mechanisms by which erroneous AI feedback impacts students’ learning, we conducted surveys and interviews that recorded students’ interactions with a short-answer AI autograder for ``Explain in Plain English'' code reading problems. Using causal modeling, we inferred the learning impacts of wrong answers marked as right (false positives, FPs) and right answers marked as wrong (false negatives, FNs). We further explored explanations for the learning impacts, including errors influencing participants’ engagement with feedback and assessments of their answers’ correctness, and participants’ prior performance in the class. FPs harmed learning in large part due to participants’ failures to detect the errors. This was due to participants not paying attention to the feedback after being marked as right, and an apparent bias against admitting one’s answer was wrong once marked right. On the other hand, FNs harmed learning only for survey participants, suggesting that interviewees’ greater behavioral and cognitive engagement protected them from learning harms. Based on these findings, we propose ways to help learners detect FPs and encourage deeper reflection on FNs to mitigate learning harms of AI errors. 

Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.