Helps you optimize your life

Planning

The 1Cademy AI Assistant is a comprehensive scheduling tool that seamlessly integrates with your Google Calendar to optimize and automate the management of your tasks, meetings, and events. It is constantly aware of your changing schedule, adapts to changes in time and priority, and integrates with your Learning Management Systems to automatically schedule course-related deadlines and activities. It uses the Pomodoro technique to break down your study, practice, work, and exercise sessions into manageable blocks with brief breaks to improve your productivity and efficiency. Additionally, it employs scientifically proven cognitive psychology techniques such as Spacing and Interleaving to enhance your long-term learning and retention. With 1Cademy AI Assistant, you can improve your time management, stay on top of your tasks, and boost your learning outcomes, all in one convenient location. The assistant prioritizes your tasks and meetings based on your defined deadlines and priorities, and uses a color-coding system to help you quickly assess your progress and manage your time effectively.

Meetings

The 1Cademy AI Assistant is aware of your dynamically changing schedule. When scheduling a one-to-one or group meeting, you simply provide the contact information of the individuals you wish to meet with. The assistant automatically contacts them, and requests that they specify their preferred time slots on a visual calendar of your availabilities, without disclosing any of your tasks or events to them. The assistant also sends reminders to the invitees in case they miss the original invitation. Once the invitees have specified their availabilities, the assistant identifies the most suitable time slots that work for the majority, sets it in your calendar, and sends out Google Calendar invitations to all the attendees. Furthermore, for both one-time and recurring meetings, the assistant can schedule a Google Meet or Zoom call based on your preference. Additionally, the assistant can attend the meeting, transcribe the conversation, and send out a report of the main topics discussed and the results of brainstorming to all the participants after the meeting.

Goals

The 1Cademy AI heroCanvasDimensionsAssistant is designed to help you make steady progress towards your goals and objectives, both personal and academic. It utilizes a unique point system to motivate you to form beneficial habits and recognize how these habits can improve your life. The assistant rewards you with badges for completing tasks and maintaining good habits, which serves as a visual representation of your progress. Additionally, it tracks your progress towards each goal, and provides you with personalized feedback and guidance to help you focus on areas where you need improvement and to remind you of your strengths. This way, the assistant helps you achieve a more balanced and well-rounded life, where you excel in all aspects.

1Cademy AI assistant motivates the development of valuable habits.

The 1Cademy AI assistant not only encourages the formation of beneficial habits, but also intelligently recognizes how these habits can improve your life and motivates you to invest more time in them.

1Cademy AI assistant motivates the development of valuable habits.

Your assistant checks your existing schedule to optimize time-allocation for your tasks. It also allows you to easily reschedule tasks to different times or days if you are unable to complete them.

Auto-schedules your tasks.

Your assistant knows everybody’s schedules and shares a subset of their availability to allow them to easily coordinate meetings. It highly values privacy and does not share the details of everything in users’ schedules.

Schedules your 1-to-1 and group meetings.

Your assistant knows the courses you’re taking, your assignment deadlines, classes, and exams. It motivates you to study and finish your assignments on time. Also, it keeps an eye on your assignment/exam scores and prioritizes your future activities accordingly.

Is in sync with your instructors.

You can use all the capabilities of your AI assistant without paying a penny for as long as you wish. We are a research group at the University of Michigan and Honor Education. Our only intentions are to improve human life and education. Our research is done using anonymous data and we do not share your personal data with any third parties.

An AI Assistant for Free!!!

When scheduling your tasks and meetings, your assistant always keeps an eye on the deadlines and priorities you define for your goals and activities.

Schedules tasks and meetings based on you priorities.

Your assistant color-codes your tasks/habits based on how close they are to their deadlines. This allows you to quickly depict your progress and assess your available time to finish each task, directly in your Google Calendar.

Signals how close you are to each deadline.

Your assistant acknowledges your hard work by rewarding you with badges along the way from completing tasks and keeping up with good habits.

Motivates through points and badges.

You can easily drag your tasks and events around, directly in your Google Calendar or your Personal Assistant page. 1Cademy assistant will seamlessly assist you even if you are not in the app. It is aware of the adjustments in your schedule whenever and wherever you change them, so it can adapt your future plans accordingly.

Allows you to easily modify your schedule.

Your assistant schedules your studying/practice based on “desirable difficulties.” Desirable difficulties, such as spacing and interleaving, are study/practice scheduling techniques proven to significantly improve long-term learning, in all age and ethnic groups, genders, and across verbal, mathematical, motor, visual, and inductive learning.

Uses scientifically proven techniques to boost your long-term  learning.

Pomodoro technique is used in Scrum and Agile management methods. It sets blocks of productive time with short breaks in-between to ensure that productive time is not interrupted but that there is adequate time off to prevent fatigue or burn-out.

Leverages methods to mitigate procrastination and reduce burn-out.

Prospect theory explains that humans are more sensitive to incremental gains (and losses) compared to the same total value gained (lost) at once. Loss aversion explains that humans are more sensitive to losing than gaining the same value. 1Cademy assistant helps you break your tasks/habits into small pieces to perceive more success than accomplishing the entire goal at once. It also makes your losses as prominent as your gains to motivate you to learn from your losses.

Leverages the psychology of motivation.
1Cademy AI assistant motivates the development of valuable habits.

1Cademy Notebook

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1Cademy is a platform that aims to improve the efficiency of learning and research by utilizing a collaborative approach to gather information from various sources and organize it into concise notes that focus on a single concept.

These notes are granularly organized and visualized as a knowledge graph that illustrates the hierarchical relationships between concepts. The platform uses a peer-review process, reputation system, and voting mechanism to ensure the quality of the knowledge graph and encourage the development of high-quality content.

Through this process, students and researchers can improve upon each other's contributions, propose more up-to-date and user-friendly versions of each note and share their learning perspectives.

Over the past two years, 1,543 students and researchers from 183 institutions have participated in the platform, resulting in the formation of 49 research and learning communities covering a wide range of subjects.

The 1Cademy AI Assistant is designed to improve human life and education by promoting the development of beneficial habits and scheduling tasks and meetings.

The assistant recognizes the positive impact of these habits on one's life and motivates the user to invest more time in them. It auto-schedules tasks and optimizes time-allocation, schedules 1-to-1 and group meetings, and keeps the user in sync with their instructors by providing information on courses, assignment deadlines, classes, and exams.

It also provides real-time updates on the user's progress on tasks and deadlines and rewards them with points and badges. Additionally, it employs techniques such as desirable difficulties and Pomodoro to boost long-term learning and mitigate procrastination and burn-out.

Furthermore, it leverages the psychology of motivation by breaking tasks and habits into small pieces and making losses as prominent as gains to motivate the user to learn from their mistakes.

Sponsors

Our Team

Iman YeckehZaare - 1Cademy Architect

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Iman YeckehZaare is the founder and architect of 1Cademy. He is currently pursuing his Ph.D. at the University of Michigan, School of Information. He has a Master of Science Degree in Information Science with two specializations in Human-Computer Interaction (HCI) and Information Economics for Management (IEM) from the same institution. Additionally, Iman holds two Bachelor of Engineering Degrees in Computer Science and Information Technology.

Iman was awarded the title of Best Graduate Student Instructor of the Year 2018-2019 at the University of Michigan, School of Information. He was also a Michigan I-Corps 2013 Graduate, a Campus of the Future 2018 Semi-finalist, an Innovation in Action 2018 2nd Prize awardee, and a Learning Levers 2019 3rd Prize awardee.

1Cademy Architect

Iman YeckehZaare

Paul Resnick holds the esteemed position of Michael D. Cohen Collegiate Professor of Information, Associate Dean for Research and Innovation, and Professor of Information at the University of Michigan's School of Information. As a trailblazer in the fields of recommender systems and reputation systems, he played a pivotal role in developing the award-winning GroupLens Collaborative Filtering Recommender system, which received the 2010 ACM Software Systems Award.

In recognition of his exceptional work, Resnick received the prestigious University of Michigan Distinguished Faculty Achievement Award in 2016 and the SIGCHI CHI Academy Award in 2017. Among his numerous notable publications, "The Social Cost of Cheap Pseudonyms," co-authored with Eric Friedman, earned the inaugural ACM EC Test of Time Award. Additionally, his 2012 MIT Press book, Building Successful Online Communities: Evidence-based Social Design, co-authored with Robert Kraut, made a significant impact in the field.

In 2020, Resnick was honored as an ACM Fellow for his remarkable contributions to recommender systems, economics and computation, and online communities, an honor reserved for the top one percent of ACM Members. He served as chair of the RecSys Conference steering committee from 2013 to 2015, and in 2014, co-chaired the ICWSM Conference. Resnick obtained his Ph.D. from MIT in 1992. He has been an advisor to the 1Cademy project since 2013.

1Cademy Advisor

Paul Resnick

Joel Podolny, a distinguished sociologist and CEO of Honor Education, Inc., has an impressive background in academia and corporate training. Previously, he held the position of Vice President at Apple and was the founding Dean of Apple University (2009-2021), where he managed the company's internal training program for employees and executives. This program instilled a deep understanding of Apple's culture, values, and innovative mindset. Apple's online learning program, designed to provide continuous educational opportunities for all employees, has become an indispensable resource for the organization. The comprehensive curriculum covered a wide range of subjects and was crafted by an exceptional group of educators, industry practitioners, and Apple veterans.

Before working at Apple, Podolny served as Dean and Professor of Management at the Yale School of Management (2005-2008), leading a significant overhaul of the Yale MBA curriculum to better equip students for the intricate, cross-functional global landscape. Prior to his tenure at Yale, he held positions as a Professor of Business Administration and Sociology at Harvard Business School (2002-2005) and as a Professor of Organizational Behavior and Strategic Management at Stanford Graduate School of Business (1991-2002). At Stanford, he served as Senior Associate Dean and taught courses in business strategy, organizational behavior, and global management. Podolny earned his Ph.D. in Sociology from Harvard University in 1991.

1Cademy Advisor

Joel Podolny

Roby Harrington is currently a board member of the Camphill Foundation, an advisor to CORE ECON, a board member of governors at Stanford University Press, a special advisor to the CEO of Honor Education Technology, and a farmer at Ten Barn Farm in Ghent, NY. At W. W. Norton & Company, Inc, Roby held various positions, including sales representative (1979-82), editor of political science, philosophy, and religion (1983-2020), national sales manager (1987-93), director of the college department (1994-2020), and Vice Chairman (2007-2021). He was also the chairman of the board at Camphill Foundation (2015-2020) and a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University (2020-2021).

1Cademy Advisor

Roby Harrington

Related Research

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

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Collaborative question generation and mapping by students has been shown to improve students' active (engaged) and meaningful learning. In implementing these methods into a course that enabled high levels of autonomy (as recommended by Self-determination theory), we found student procrastination to be a persistent problem. Since student contribution tends to wane over the course of a semester, the efficacy of a curriculum reliant on content generation similarly wanes. We describe our efforts in reducing procrastination in a course focused on collaborative question generation and mapping, using an iterative design research methodology, over eight months of two semesters. To encourage students to create high-quality questions, we implemented a voting system that graded students based on the number of instructor up-votes given to questions they created. While this reduced procrastination early on, students found ways to take advantage of the autonomy provided by the course curriculum, leading them to resume procrastinating. To address this issue, we adjusted the grading schema for the second semester by scoring both students' and instructors' up-votes, while also allowing students to present their created content in optional weekly meetings. The introduction of optional weekly meetings had the greatest effect towards decreasing procrastination. End-of-semester surveys reported that: 1) while students enjoyed both semesters, they gave more positive feedback for the second semester; 2) student-led discussions for personally created content, when combined with the autonomy to choose both the topic and time to study, helped their learning and time management skills.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Desirable difficulties such as retrieval practice (testing) and spacing (distributed studying) are shown to improve long-term learning. Despite their knowledge about the benefits of retrieval practice, students struggle with application. We propose a mechanism of embedding desirable difficulties in the classroom called "retrieval-based teaching." We define it as asking students many ungraded, granular questions in class. We hypothesized that this method could motivate students to (1) study more and (2) increase the spacing of their studying. We tested these two hypotheses through a quasi-experiment in an introductory programming course. We compared 684 students' granular activities with an interactive eBook between the class discussion sections where the intervention was implemented and the control discussion sections. Over four semesters, there were a total of 17 graduate student instructors (GSIs) that taught the discussion sections. Each semester, there were five discussion sections, each taught by a distinct GSI. Only one of the five per semester implemented the treatment in their discussion section(s) by dedicating most of the class time for retrieval-based teaching. Our analysis of these data collected over four consecutive semesters shows that retrieval-based teaching motivated students to space their studying over an average of 3.78 more days, but it did not significantly increase the amount they studied. Students in the treatment group earned an average of 2.36 percentage points higher in course grades. Our mediation analysis indicates that spacing was the main factor in increasing the treated students' grades.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Prior literature suggests that computer science education (CSE) was less affected by the pandemic than other disciplines. However, it is unclear how the pandemic affected the quality and quantity of students' studying in CSE. We measure the impact of the pandemic on the amount and spacing of students' studying in a large introductory computer science course. Spacing is defined as the distribution of studying over multiple sessions, which is shown to improve long-term learning. Using multiple regression models, we analyzed the total number of students' interactions with the eBook and the number of days they used it, as a proxy for studying amount and spacing, respectively. We compared two sequential winter semesters of the course, one during (Winter 2021) and one prior to the pandemic (Winter 2020). After controlling for possible confounders, the results show that students had 1,345.87 fewer eBook interactions and distributed their studying on 2.36 fewer days during the pandemic when compared to the previous semester prior to the pandemic. We also compared four semesters prior to the pandemic (Fall and Winter of 2018 and 2019) to two semesters during the pandemic (Fall 2020 and Winter 2021). We found, on average, students had 3,376.30 fewer interactions with the eBook and studied the eBook on 16.35 fewer days during the pandemic. Contrary to prior studies, our results indicate that the pandemic negatively affected the amount and spacing of studying in an introductory computer science course, which may have a negative impact on their education.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Spacing and procrastination are often thought of as opposites. It is possible, however, for a student to space their studying by doing something every day throughout the semester and still procrastinate by waiting until late in the semester to increase their amount of studying. To analyze the relationship between spacing and procrastination, we examined 674 students’ interactions with a course eBook over four semesters of an introductory programming course. We measured each student’s semester-level spacing as the number of days they interacted with the eBook, and each student’s semester-level procrastination as the average delay from the start of the semester for all their eBook interactions. Surprisingly, there was a small, yet positive, correlation between the two measures. Which, then, matters for course performance: studying over more days or studying earlier in the semester? When controlling for total amount of studying, as well as a number of academic and demographic characteristics in an SEM analysis, we find a strong positive effect of spacing but no significant effect of procrastination on final exam scores.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Extensive prior research shows that spacing – the distribution of studying over multiple sessions – significantly improves long-term learning in many disciplines. However, in computer science education, it is unclear if 1) spacing is effective in an incentivized, non-imposed setting and 2) when incentivized, female and male students space their studying differently. To investigate these research questions, we examined how students in an introductory computer science course (378 female and 310 male) spaced their studying. A retrieval practice tool in the course (for 5% of the course grade) incentivized students to space their studying, by awarding a point per day of usage. To measure how much each student spaced, we examined their interactions with the course eBook, which served as their primary learning resource. Specifically, when comparing two students with the same academic and demographic characteristics, the same measure of course easiness, and the same amount of content studied, we considered the student who distributed their studying over more days to be the one who spaced more. Using this definition, our structural equation modeling (SEM) results show that, 1) on average, students who spaced their studying over 14.516 more days (one standard deviation) got 2.25% higher final exam scores; and 2) female students spaced their studying over 4.331 more days than their male counterparts. These results suggest that, in an introductory computer science course, incentivized spacing is effective. Notably, when compared to their male counterparts, female students both exhibited more spacing and obtained higher final exam scores through spacing.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Generating multiple-choice questions is known to improve students' critical thinking and deep learning. Visualizing relationships between concepts enhances meaningful learning, students' ability to relate new concepts to previously learned concepts. We designed and deployed a collaborative learning process through which students generate multiple-choice questions and represent the prerequisite knowledge structure between questions as visual links in a shared map, using a variation of Concept Maps that we call "QMap." We conducted a four-month study with 19 undergraduate students. Students sustained voluntary contributions, creating 992 good questions, and drawing 1,255 meaningful links between the questions. Through analyzing self-reports, observations, and usage data, we report on the technical and social design features that led students to sustain their motivation.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

Retrieval practice, spacing, and interleaving are known to enhance long-term learning and transfer, but reduce short-term performance. It can be difficult to get both students and instructors to use these techniques since they perceive them as impeding initial student learning. We leveraged user experience design and research techniques, including survey and participant observation, to improve the design of a practice tool during a semester of use in a large introductory Python programming course. In this paper, we describe the design features that made the tool effective for learning as well as motivating. These include requiring spacing by giving credit for each day that a student answered a minimum number of questions, adapting a spaced repetition algorithm to schedule topics rather than specific questions, providing a visual representation of the evolving schedule in order to support meta-cognition, and providing several gameful design elements. To assess effectiveness, we estimated a regression model: each hour spent using the practice tool over the course of a semester was associated with an increase in final exam grades of 1.04%, even after controlling for many potential confounds. To assess motivation, we report on the amount of practice tool use: 62 of the 193 students (32%) voluntarily used the tool more than the required 45 days. This provides evidence that the design of the tool successfully overcame the typically negative perceptions of retrieval practice, spacing, and interleaving.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

In an introductory Python programming course intended for non-majors with little prior CS experience, with 85 male and 108 female students, we were able to capture electronic traces of students' studying and problem-solving. There was no significant difference in final exam scores by gender but we found that female students spent 12.1 more hours studying over the semester while male students on average earned 2.7 more points per hour of solving problem set questions over the first half of the semester. We were able to capture their learning behavior because students studied using the Runestone interactive textbook and completed weekly problem sets in the same platform for the first half of the semester. We analyzed these logs to determine three quantities for each student. One is study time, as measured by total use of Runestone outside of weekly assignments. The second is speed, as measured by the number of points students earned per hour working on problem sets. The third is earliness, as measured by how far before the deadlines they worked on weekly assignments. We conclude that male students were faster at completing problem sets early in the semester but that female students found an alternative pathway to success.

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.

The Runestone ebook platform is open source, extensible, and already serves over 25,000 learners a day. The site currently hosts 18 free ebooks for computing courses. Instructors can create a custom course from any of the existing ebooks on the site and can have their students register for that custom course. Instructors can create assignments from the existing material in each ebook, grade assignments, and visualize student progress. Instructors can even create new content for assignments. The Runestone ebooks contain instructional material and a variety of practice problem types with immediate feedback. One of the practice types, Parsons problems, is also adaptive, which means that the difficulty of the problem is based on the learner’s performance. Learner interaction is recorded and can be analyzed. This paper presents the history of Runestone, describes the interactive features, summarizes the previous research studies, and provides detail on the recorded data. Interaction data can be shared with other learning environments through the Learning Tools Interoperability Standard (LTI).

ACM SIGCSE 2023: Reducing Procrastination Without Sacrificing Students' Autonomy Through Optional Weekly Presentations of Student-Generated Content.