TUMO Labs

About the Project

When “self-service” places responsibility on users to accomplish their tasks, this handover of agency comes with a burden. Users must adjust to systems, instead of systems adjusting to what users need. Even documentation or support still leaves the user primarily on their own to figure things out. Self-service experiences tend to be “one-size fits-all” with minimal or no consideration of the underlying multi-level needs, goals, and feelings of the user.

This project aims to redefine self-service experiences to be truly about the “self” through the power of empathy that will take into consideration the user’s goals and issues, as well as information from their social environment.

The “Future of Self-Service” project by TUMO Labs and the world’s largest enterprise software company consists of 11 challenges, to which you may apply as an individual or in a team.

Project Start Date: Nov 5, 2021
Duration: 3-4 months
Language: English
Format: Online
Deadline to apply: October 31, 2021
Fee: Tuition-free
Eligibility: Anyone over 18 with the required pre-existing skills, individually or in a team.

Attention: it’s possible to apply to multiple challenges, but you must choose one for your final project.

Registration is closed

The participants of the challenge will explore the concept of adaptive software systems and user interfaces from a market perspective. User interfaces should include both “traditional” graphical interfaces as well as other forms, such as voice interfaces.

The research can be broken down into several questions:

  • Who are the main industry actors supporting or engaging with adaptive software systems, particularly user interfaces?
  • What kinds of applications are already incorporating this principle, and why?
  • What is the pain point they are trying to solve through adaptation and what is the benefit of this approach?
  • Are there other applications involving adaptive interfaces and: (1) decision-making; (2) self-service?
  • What industries, applications, or use cases may benefit the most from this?

Expected outcome:

  • Market analysis.
  • A collection of applications and use cases incorporating this principle.
  • Particularly, should include/highlight applications with decision-making and/or self-service aspects.
  • Ranking of use cases based on most opportunity/benefit from this paradigm.

Prerequisites:

Students with a business or related studies background interested in software; alternatively, computer science or related studies (e.g., business informatics), students able to conduct market research.

Participants in this challenge will explore the concept of adaptive software systems, particularly user interfaces, from an academic and technology perspective.

The research can be broken down into the following questions:

  • What is the current state of research into adaptive software systems and interfaces?
  • What are emerging trends or directions for research into this space and adjacent spaces?
  • What are the technical guidelines and best practices proposed in research?

Expected outcome:

  • Literature review from the origin of adaptive systems research and similar concepts to today.
  • Identification of branching or adjacent research directions to user adaptation in software systems.
  • Technical guidelines or best practices based on the literature review.

Prerequisites:

Students with a computer science, design, business, or related studies background.

User assistance for software is provided through various forms such as help documentation, in-app glossaries, app walk-throughs, videos and the like. While it can be a valuable source of information in addition to the software, it can also be used to mitigate or obscure interface or experience design weaknesses. For self-service experiences, users may be provided some form of user assistance to guide them in the workflow.

The aim of this topic is threefold:

  1. To analyze the role of user assistance today and trends in its purpose for users.
  2. To gather predictions or expectations for how user assistance may look in the future.
  3. To explore the role of adaptive interface concepts for user assistance experiences.

Expected outcome:

  • Analysis of user assistance and trends in the software industry.
  • Prediction of where current or future trends may take user assistance in the future (~2030), incorporating analysis of trends in related fields such as UI/UX design.
  • Proof-of-concept demonstrating opportunities for how increased data about the user (e.g., emotion detection) can be used to adapt user assistance.
  • E.g., incorporating an emotion detection API, likely based on user peripheric input data, to modify some form of help documentation, chat box, or some other medium user assistance can be provided by.

Prerequisites:

Students with some experience in (web) development and software engineering interested in UI/UX/user assistance topics. Understanding of basic machine learning would be a benefit if deciding to incorporate data from an emotion detection API or a similar source.

This research topic is composed of several parts:

  1. Definition of approach to capture user behavioral data within graphical user interfaces, ideally an interface part of an existing SAP application.
  2. Development or use of existing tool to capture the identified data (e.g., keyboard and mouse; browser plugin; etc).
  3. Recruitment of users to execute actions in an application with similar functionality to enterprise software (e.g., filling out a timesheet) and provide feedback on their internal state during the data capture (e.g., with an additional survey to capture emotion and other behavioral information).
  4. Exploration of the dataset to identify possible features of the user context.

Expected outcome:

  • Definition of data collection approach and methodology.
  • Creation of a dataset.
  • Learnings and best practices from methodological approach to dataset creation.
  • Data exploration findings with regards to inferring user context.
  • Feasibility analysis.

Prerequisites:

Students with a data science, computer science, or related studies background.

This topic aims to explore how self-service experiences and empathy can extend outside of corporate workstations to include devices like autonomous vehicles. In particular, the research should consider autonomous vehicles from a software perspective similar to how mobile devices are “extensions of ourselves” today, yet they clearly do have a real-world physical presence. Therefore, while some applications that are safety-oriented are expected, the research should also aim to identify other applications for incorporating empathy through user understanding within this context.

Expected outcome:

  •  Analysis of data collection possibilities (e.g., through sensors) in the vehicle context.
  • Conceptualization of the vehicle as an interface to identify potential applications for demonstrating empathy, particularly with regards to self-service.
  • Proof-of-concept of choice of application showing the collection and processing of user data, inference of appropriate adaptation, and some result or benefit for a user with a primary focus on demonstrating feasibility and identifying novel experiments and data opportunities.

Prerequisites:

Students with a data science, computer science, or related studies background. Preferably, students with an interest in mobility, self-driving vehicles, and user interfaces that extend beyond the typical graphical UIs. Students with a business background or business informatics may also be suitable depending on their interest in software development.

The goal of this research is to investigate common accessibility barriers in enterprise self-service workflows today. Furthermore, the research should connect the concept of adapting systems based on the user context and needs to improve accessibility or novel accessibility approaches with the self-service context.

Expected outcome:

  • Proof-of-concept or demonstration of accessibility principles within self-service workflows that can benefit from the adaptive system paradigm, e.g., with a UI mockup.
  • Definition of best practices or guidelines for accessibility with regards to self-service.

Prerequisites:

Students with a design, computer science, data science, or related studies background. Preferably, students with an interest in UI/UX and accessibility topics.

The goal of this research is to investigate how hand-labelling of purpose-generated datasets in a business context can be done for affective computing and similar contextual analyses.

Expected outcome:

  • Definition of methodology and exploration of various labelling approaches for data.
  • Evaluation of accuracy from comparing the different approaches.

Prerequisites:

Students with a data science, computer science, or related studies background.

The goal of this topic is to survey user interface trends today to create a possible map towards future interfaces in 2030. Based on the possible directions for user interfaces, the second step would be to look at implications of these interface trends for enterprise software considering.

Expected outcome:

  • Survey of user interface trends
  • Predictions or projections of what trends may be most definitive for user interfaces in the next 5 and 10 years
  • Impact analysis for enterprise software
  • Possible UI/UX mockup or prototype showcasing a future enterprise software interface interaction experience

Prerequisites:

Students with a design, computer science, or related background.

This research problem requires measuring variability in user behavior and preferences in enterprise or enterprise-like applications (e.g., an IDE or Excel) across different dimensions within a target population of adult (18+) software users who have a degree of ICT proficiency comparable to a typical enterprise software user.

Therefore, this challenge involves design of a study, participant recruitment, experimental measurements, and statistical analysis.

Expected outcome:

  • Variability measure of user behavior and preferences across different individuals.
  • Variability measure of user behavior and preferences for one individual across different time periods (e.g., after increasing application use).
  • Variability measure of user behavior and preferences for one individual across different tasks.
  • Comparison of variability measures and statistical analysis.

Prerequisites:

Students with a statistics, mathematics, data science, computer science, or related background.

The goal of this research is to explore how historical data points of user behavior in software interfaces may have weaker correlations with user behavior at a future moment in time.

Expected outcome:

  •  Definition of typical features used for user modeling, particularly for behavior, preference inference, and affective computing.
  • Evaluation of expiration periods or changes in relevance for discrete data elements over time or changes in context for a single individual.
  • Comparison of results across multiple individuals with statistical analyses.
  • Classification of user data points and features based on most predictive stability and estimation of “expiry bands”.

Prerequisites:

Students with a statistics, mathematics, data science, computer science, or related background.

Various modeling approaches exist for user behavior and preferences, ranging from broad classifications by a certain personality trait (e.g., introversion/extraversion) like personality-based agents to character computing to other approaches. The output of these various models is used for understanding and predicting user behavior.

The challenge is to first identify different granularity levels for user modeling based on research (e.g., personality-based agents) applied for uses like preference inference or interface adaptation. Then, the different approaches should be compared with regards to both input needs (e.g., data volume or particular data sources) and output results (e.g., inference quality).

Expected outcome:

  • Literature review of user modeling approaches applied for preference inference, interface adaptation, and related purposes.
  • Comparison of different modeling approaches.

Prerequisites:

Students with a data science, computer science, mathematics, or related background.

The purpose is to explore how user interaction with consuming a single artifact, e.g., reading a technical document, can be modified by adjusting attributes of the artifact (e.g., presentation attributes like layout) based on their preferences with consideration of static vs. dynamic preferences, explicit vs. implicit preferences, and so forth. The user experience with the artifact should then be extended to also study experiences with other artifacts or the same artifact over time.

Expected outcome:

  • Definition of adaptive attributes for an artifact and inference of user preferences.
  • Proof-of-concept adapting the artifact based on user preference inference, e.g., using natural language generation.
  • Extension of the above for interaction with the same artifact across different moments in time and comparison of attributes – were the same ones adapted, in the same way, and so forth; extension to interaction with different artifacts.

Prerequisites:

Students with a data science, computer science, mathematics, or related background.