Welcome to our design space for intelligent and interactive writing assistants! The design space consists of five aspects: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension). Please refer to our paper for the detailed definitions of each dimension and code.
To create this design space, we collaborated with researchers from a variety of disciplines, including Human-Computer Interaction (HCI), Natural Language Processing (NLP), Information Systems, and Education, and annotated 115 papers from HCI and NLP fields to understand the current landscape of writing assistants. We hope that our design space offers researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants.
Our design space is a living artifact, as it will evolve over time alongside the fields. We invite the community to contribute to this artifact by adding new papers, annotations, and discussions to track future developments in this space.
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2024 | Drafting, Revision | Technical, Professional, Personal | Descriptive, Persuasive, Entertainment | Specific Objectives | Specified | Profession | Profession | Agency, Ownership, Trust, Transparency | Agency, Ownership, Trust, Transparency | Foundation model | Tool | Human evaluation | Linguistic quality, Style & adequacy | Latency | Explicit | Selection | Text editor | Writing area | Formatting, Location | Tool | User-initiated, System-initiated | Proposal | Model | Input text | Usability consistency | Free and open-source software | ||||||||||||||
2023 | Revision | Professional | Feedback, Persuasive | Detailed Requirements, General Direction | Specified | Textual coherence | Textual coherence | Ownership, Availability, Trust | Ownership, Availability, Trust, Agency | Writing expertise | Writing expertise | Expert | Unknown | Statistical ML model, Rule-based model | Data | Regression | Supervised learning | Fine-tuning | Machine-learned evaluation | Linguistic quality | Cost | Implicit, Explicit | Inspiration, No integration | Other, Text editor | Separated | Location | Tool | User-initiated | Analysis | Deterministic | Input text | Designing with stakeholders | Writing | |||||||
2023 | Machine, Expert | Large (<1M) | Foundation model | Data | Generation | Supervised learning | Fine-tuning | Automatic evaluation, Human evaluation | Linguistic quality | Cost | ||||||||||||||||||||||||||||||
2023 | Revision | Professional, Academic | Feedback, Persuasive | General Direction, Specific Objectives | Implied | Education | Education | Textual coherence | Textual coherence | Ownership | Ownership | Writing expertise | Writing expertise | User, Machine | Large (<1M) | Deep neural network | Data | Classification | Supervised learning | Fine-tuning | Automatic evaluation | Linguistic quality | Cost | |||||||||||||||||
2023 | Drafting | Other | Descriptive | Specific Objectives | Specified | Other, Profession | Other, Profession | Textual diversity, Explainability | Textual coherence, Explainability | Other | Other | Efficiency | Efficiency | Rule-based model | Tool | Human evaluation | Style & adequacy | No control | Inspiration, No integration | Text editor | Separated | Location | Tool | System-initiated | Proposal, Analysis | Curated | Input text | Designing with stakeholders | Writing | |||||||||||
2023 | Revision | Professional | Educational, Persuasive | Detailed Requirements, Specific Objectives | Specified | Education | Education | Other, Explainability | Other, Explainability | Ownership, Trust | Ownership, Trust | Writing expertise, Other | Writing expertise, Other, Efficiency | Expert | Large (<1M) | Foundation model | Data | Classification | Supervised learning | Fine-tuning | Human evaluation, Automatic evaluation | Other | Implicit | Inspiration | Text editor | Separated | Location, Media type, Formatting | Tool | User-initiated | Analysis | Model | Input text | Designing with stakeholders | Conventions | Writing | |||||
2023 | Profession | Personalization | Ownership | Availability, Trust | Writing expertise | Designing with stakeholders | Conventions | |||||||||||||||||||||||||||||||||
2023 | Revision | Feedback | Detailed Requirements | Language & culture | Textual coherence | Textual coherence | Writing expertise | Writing expertise | Expert, Other, User | Extremely large (>1M) | Foundation model | Rewriting, Classification | Supervised learning | Fine-tuning | Automatic evaluation, Human-machine evaluation | Linguistic quality, Style & adequacy | Analysis | Model | Input text | |||||||||||||||||||||
2023 | Language & culture, Profession | Other | Other | Efficiency | Authors | Medium (<10k) | ||||||||||||||||||||||||||||||||||
2023 | Revision | Academic | Feedback, Persuasive | Detailed Requirements, Specific Objectives | Implied | Education | Education | Other | Writing expertise, Other | Writing expertise, Other | User, Expert | Medium (<10k) | Foundation model, Deep neural network | Classification | Supervised learning | Fine-tuning | Automatic evaluation, Human evaluation, Human-machine evaluation | Other |
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Authors: Mina Lee , Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum, Vipul Raheja, Hua Shen, Subhashini Venugopalan, Thiemo Wambsganss, David Zhou, Emad A. Alghamdi, Tal August, Avinash Bhat, Madiha Zahrah Choksi, Senjuti Dutta, Jin L.C. Guo, Md Naimul Hoque, Yewon Kim, Seyed Parsa Neshaei, Agnia Sergeyuk, Antonette Shibani, Disha Shrivastava, Lila Shroff, Jessi Stark, Sarah Sterman, Sitong Wang, Antoine Bosselut, Daniel Buschek, Joseph Chee Chang, Sherol Chen, Max Kreminski, Joonsuk Park, Roy Pea, Eugenia H. Rho, Shannon Zejiang Shen, Pao Siangliulue
Core group of annotators: Avinash Bhat, Simon Buckingham Shum, Agnia Sergeyuk, Yewon Kim, David Zhou, Emad A. Alghamdi, Jin L.C. Guo, Seyed Parsa Neshaei, Hua Shen, Md Naimul Hoque, Madiha Zahrah Choksi, Katy Ilonka Gero, Sarah Sterman, Antonette Shibani, Mina Lee
Designer of this artifact: Shannon Zejiang Shen, Mina Lee