Recent years have brought immense advancements around development of Artificial Intelligence (AI) technology and its application. This has also led to a boost in interest on human-AI collaboration and co-creation. Specifically in the creative field, where it is usually not sufficient for an AI to solve given problems or produce deterministic output, this next-level interaction between humans and AI comes with huge potential but also major challenges, related to aspects such as role and power distribution, trust and reliance, or efficiency and effectiveness. In this paper, we present a pilot study on human-AI collaboration in three different creative fields (programming, marketing texting, and UI design), addressing User Experience, Technology Acceptance and, specifically, Perception of Collaboration. The study is based on a theoretical framework we derived from prior research through a focused, systematic literature review, and intended to raise research questions and identify related hypotheses informing future empirical work.

Existing research indicates that students prefer to work with tutors who express politely in online human-human tutoring, but excessive polite expressions might lower tutoring efficacy. However, there is a shortage of understanding about the use of politeness in online tutoring and the extent to which the politeness of instructional strategies can contribute to students’ achievement. To address these gaps, we conducted a study on a large-scale dataset (5,165 students and 116 qualified tutors in 18,203 online tutoring sessions) of both effective and ineffective human-human online tutorial dialogues. The study made use of a well-known dialogue act coding scheme to identify instructional strategies, relied on the linguistic politeness theory to analyse the politeness levels of the tutors’ instructional strategies, and utilised Gradient Tree Boosting to evaluate the predictive power of these politeness levels in revealing students’ problem-solving performance. The results demonstrated that human tutors used both polite and non-polite expressions in the instructional strategies. Tutors were inclined to express politely in the strategy of providing positive feedback but less politely while providing negative feedback and asking questions to evaluate students’ understanding. Compared to the students with prior progress, tutors provided more polite open questions to the students without prior progress but less polite corrective feedback. Importantly, we showed that, compared to previous research, the accuracy of predicting student problem-solving performance can be improved by incorporating politeness levels of instructional strategies with other documented predictors (e.g., the sentiment of the utterances).

Committing to a major is a fateful step in an undergraduate's education, yet the relationship between courses taken early in an academic career and ultimate major choice remains little studied at scale. We analyze transcript data capturing the academic careers of 26,892 undergraduates at a private university between 2000 and 2020. We forecast students' terminal major on the basis of course-choice sequences beginning at university entry. We represent course enrollment history using natural-language methods and vector embeddings. We find that a student's very first enrolled course predicts their terminal major thirty times better than random guessing and more than a third better than majority class voting.

We explore the relationship between video playback speed and student learning outcomes. Using an experimental design, we present the results of a pre-registered study that assigns users to watch videos at either 1.0x or 1.25x speed. We find that students who consume sped content are more likely to get better grades in a course, attempt more content, and obtain more certificates. We also find that when videos are sped up, students spend less time consuming videos and are marginally more likely to complete more video content. These findings suggest that future study of playback speed as a tool for optimizing video content for MOOCs is warranted. Applications for reinforcement learning and adaptive content are discussed.

College courses are often organized into hierarchical sequences, with foundational courses recommended or required as prerequisites for other offerings. While the wisdom of particular sequences is usually ascertained on the basis of faculty experience or student peer networks, machine learning techniques and ubiquitous transcript data make it possible to systematically identify the courses that best predict subsequent high achievement across entire curricula and student populations. We demonstrate the utility of this approach by analyzing five years of course sequences and earned grades for 13,218 undergraduates enrolled in courses with substantial quantitative content at a private research university. Findings indicate that prior completion of specific courses is positively associated with success in subsequent target courses, and suggest that academic planning could be enhanced through scaled observation of the revealed benefits of course sequences.

MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted interventions can be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding.