TOWARD SYMBIOTIC HUMAN-AI INTERACTION FOCUSING ON PROGRAMMING BY EXAMPLE

Programming has become a new literacy, but is still inaccessible to ordinary people. Programming-by-example (PBE) is an alternative approach that allows people to teach computers repetitive tasks by demonstrating couple input and output examples of the tasks. While the advancements of PBE have been mainly driven by algorithmic improvements, a growing community of researchers started realizing the importance of issues on the human side of PBE. For instance, inexperienced users often find it hard to provide complete and consistent examples, which is crucial for computers to learn the correct programs. Unfortunately, most PBE systems have limited ways to communicate with users about what it can or cannot do, and how to handle unsuccessful situations. The lack of symbiotic interaction between human users and PBE engines remain as a major hurdle against a widespread adoption of PBE techniques. To address the issues on the human side of PBE, this dissertation has four research threads. First, we began with two formative studies to establish a better understanding of inexperienced users' needs and mental models. Second, based on the findings of the formative studies, we developed a Visual Environment for Symbiotic Programming, called VESPY. VESPY interleaves visual programming and PBE techniques, enabling users (1) to decompose complex tasks into small modules on its 2-d grid, and (2) to complete each module by providing input and output examples. Four sample programs demonstrate VESPY's remarkable versatility. However, we also noticed that VESPY still had a number of usability issues. Third, to better understand the usability issues and how to help users out from common mistakes, we conducted an online user study that observed how inexperience users perform program decomposition and disambiguation, which are the two core activities of PBE. We identified seven types of mistakes, and reaffirmed that informative feedback on those mistakes is crucial for designing usable systems. Finally, we explored the design space of feedback components, in order to understand their impact on user's experience. My dissertation contributes to the AI and HCI communities with: (i) identification of unmet needs of end-users of the Web; (ii) characterization of non-programmers’ mental model; (iii) design process of interleaving visual programming and PBE; (iv) identification of mistakes people make while using PBE; and (v) design and assessment of feedback components for PBE users

Regular Schur labeled skew shape posets and their 0-Hecke modules

Assuming Stanley's $P$-partition conjecture holds, the regular Schur labeled skew shape posets with underlying set $\{1,2,\ldots, n\}$ are precisely the posets $P$ such that the $P$-partition generating function is symmetric and the set of linear extensions of $P$, denoted $\Sigma_L(P)$, is a left weak Bruhat interval in the symmetric group $\mathfrak{S}_n$. We describe the permutations in $\Sigma_L(P)$ in terms of reading words of standard Young tableaux when $P$ is a regular Schur labeled skew shape poset, and classify $\Sigma_L(P)$'s up to descent-preserving isomorphism as $P$ ranges over regular Schur labeled skew shape posets. The results obtained are then applied to classify the $0$-Hecke modules $\mathsf{M}_P$ associated with regular Schur labeled skew shape posets $P$ up to isomorphism. Then we characterize regular Schur labeled skew shape posets as the posets whose linear extensions form a dual plactic-closed subset of $\mathfrak{S}_n$. Using this characterization, we construct distinguished filtrations of $\mathsf{M}_P$ with respect to the Schur basis when $P$ is a regular Schur labeled skew shape poset. Further issues concerned with the classification and decomposition of the $0$-Hecke modules $\mathsf{M}_P$ are also discussed.Comment: 44 page

ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale, real-world interactions between students and AI systems still remain limited. In this study, we present ChEDDAR, ChatGPT & EFL Learner's Dialogue Dataset As Revising an essay, which is collected from a semester-long longitudinal experiment involving 212 college students enrolled in English as Foreign Langauge (EFL) writing courses. The students were asked to revise their essays through dialogues with ChatGPT. ChEDDAR includes a conversation log, utterance-level essay edit history, self-rated satisfaction, and students' intent, in addition to session-level pre-and-post surveys documenting their objectives and overall experiences. We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction. As a foundational step, we establish baseline results for two pivotal tasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. We finally suggest further research to refine the integration of generative AI into education settings, outlining potential scenarios utilizing ChEDDAR. ChEDDAR is publicly available at https://github.com/zeunie/ChEDDAR

RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale and real-world interactions between students and AI systems still remain limited. Addressing this gap, we present RECIPE4U (RECIPE for University), a dataset sourced from a semester-long experiment with 212 college students in English as Foreign Language (EFL) writing courses. During the study, students engaged in dialogues with ChatGPT to revise their essays. RECIPE4U includes comprehensive records of these interactions, including conversation logs, students' intent, students' self-rated satisfaction, and students' essay edit histories. In particular, we annotate the students' utterances in RECIPE4U with 13 intention labels based on our coding schemes. We establish baseline results for two subtasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. As a foundational step, we explore student-ChatGPT interaction patterns through RECIPE4U and analyze them by focusing on students' dialogue, essay data statistics, and students' essay edits. We further illustrate potential applications of RECIPE4U dataset for enhancing the incorporation of LLMs in educational frameworks. RECIPE4U is publicly available at https://zeunie.github.io/RECIPE4U/.Comment: arXiv admin note: text overlap with arXiv:2309.1324

In Vitro Chemosensitivity Using the Histoculture Drug Response Assay in Human Epithelial Ovarian Cancer

The choice of chemotherapeutic drugs to treat patients with epithelial ovarian cancer has not depended on individual patient characteristics. We have investigated the correlation between in vitro chemosensitivity, as determined by the histoculture drug response assay (HDRA), and clinical responses in epithelial ovarian cancer. Fresh tissue samples were obtained from 79 patients with epithelial ovarian cancer. The sensitivity of these samples to 11 chemotherapeutic agents was tested using the HDRA method according to established methods, and we analyzed the results retrospectively. HDRA showed that they were more chemosensitive to carboplatin, topotecan and belotecan, with inhibition rates of 49.2%, 44.7%, and 39.7%, respectively, than to cisplatin, the traditional drug of choice in epithelial ovarian cancer. Among the 37 patients with FIGO stage Ⅲ/Ⅳ serous adenocarcinoma who were receiving carboplatin combined with paclitaxel, those with carboplatin-sensitive samples on HDRA had a significantly longer median disease-free interval than patients with carboplatin- resistant samples (23.2 vs. 13.8 months, p＜0.05), but median overall survival did not differ significantly (60.4 vs. 37.3 months, p＝0.621). In conclusion, this study indicates that HDRA could provide useful information for designing individual treatment strategies in patients with epithelial ovarian cancer

RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Factors associated with hospitalization via emergency department in children with acute bronchiolitis

Purpose In infants and young children, acute bronchiolitis is a leading cause of hospitalization via emergency departments (EDs). We aimed to investigate factors associated with hospitalization via ED in children with acute bronchiolitis. Methods We reviewed medical records of children aged 36 months or younger with acute bronchiolitis who visited the ED from January to December 2017. The following clinical data were collected and analyzed: age, sex, premature birth history, symptoms, fever duration, presence of respiratory distress and radiographic lesion, and inflammatory markers. Results Of 780 children enrolled, 463 (59.4%) were hospitalized via the ED. The factor associated with the hospitalization were age ≤ 12 months (odd ratio [OR], 45.34; confidence interval [CI], 17.50-117.44), fever lasting ≥ 3 days (OR, 13.66; 95% CI, 6.46-28.87), respiratory rate ≥ 24 breaths per minute (OR, 6.88; 95% CI, 4.21-11.26), radiographic lesion (OR, 5.70; 95% CI 2.62-12.40), and chest retraction (OR, 2.45; 95% CI, 1.11-5.41). Conclusion In children with acute bronchiolitis who visit EDs, those having younger age, longer fever duration, respiratory distress or radiographic lesion may need hospitalization