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Beth Kolko

dblp:429/3164 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0003-2949-2726ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 56% Human-AI interaction · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies › digital well-being
mental well-being support
1.012026
Framing Responsible Design of AI for Mental Well-Being: AI as Primary Care, Nutritional Supplement, or Yoga Instructor? · CHI 2026
Human-AI interaction › responsible AI
responsible AI design
1.012026
Framing Responsible Design of AI for Mental Well-Being: AI as Primary Care, Nutritional Supplement, or Yoga Instructor? · CHI 2026
Health and well-being technologies
mental health
0.312026
Framing Responsible Design of AI for Mental Well-Being: AI as Primary Care, Nutritional Supplement, or Yoga Instructor? · CHI 2026

Methods — techniques the papers use, named apart from their topics

regulatory analysis · 1.0expert interviews · 1.0
YearPublicationVenuePosition
2026 Framing Responsible Design of AI for Mental Well-Being: AI as Primary Care, Nutritional Supplement, or Yoga Instructor?
abstract
Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool’s “active ingredients” for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool’s pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies—LLM tools as supplements, drugs, yoga instructors, and primary care providers—can scaffold further conversations about their responsible design.
Ned Cooper, Jose A. Guridi, Angel Hwang, Beth Kolko, Emma Elizabeth McGinty, Qian Yang 0004
CHI4