Development of a Social Robot as a Mediator for Intergenerational Gameplay & Development of a Canvas for the Conceptualisation of HRI Game Design
Bibliographic record
Abstract
Intergenerational interaction between grandparents and grandchildren benefits both \ngenerations. The use of a social robot in mediating this interaction is a relatively \nunexplored area of research. Often Human-Robot Interaction (HRI) research uses the robot \nas a point of focus; this thesis puts the focus on the interaction between the generations, \nusing a multi-stage study with a robot mediating the interaction in dyads of grandparents \nand grandchildren. \nThe research questions guiding this thesis are: 1) How might a robot-mediated game \nbe used to foster intergenerational gameplay? 2) What template can be created to conceptually describe HRI game systems? \nTo answer the first question, the study design includes three stages: 1. Human mediator Stage (exploratory); 2. The Wizard-of-Oz (WoZ) Stage (where a researcher remotely \ncontrols the robot); 3. Fully/semi-autonomous Stage. A Tangram puzzle game was used \nto create an enjoyable, collaborative experience. Stage 1 of the study was conducted with \nfour dyads of grandparents (52-74 years of age) and their grandchildren (7-9 years of age). \nThe purpose of Stage 1 was to determine the following: 1. How do dyads of grandparent-grandchild perceive their collaboration in the Tangram game? 2. What role do the dyads \nenvision for a social robot in the game? Results showed the dyads perceived high collaboration in the Tangram game, and saw the role of the robot as helping them by providing \nclues in the gameplay. The research team felt the game, in conjunction with the proposed \nsetup, worked well for supporting collaboration and decided to use the same game with a \nsimilar setup for the next two stages. Although the design and development of the next \nstage were ready, the COVID-19 pandemic led to the suspension of in-person research. \nThe second part of this thesis research focused on creating the Human-Robot Interaction Game Canvas (HRIGC), a novel way to conceptually model HRI game systems. A literature search of systematic ways to capture information, to assist in the design of the multi-stage study, yielded no appropriate tool, and prompted the creation of the HRIGC. \nThe goal of the HRIGC is to help researchers think about, identify, and explore various \naspects of designing an HRI game-based system. During the development process, the \nHRIGC was put through three case studies and two test runs: 1) Test run 1 with three \nresearchers in HRI game design; 2) Test run 2 with four Human-Computer Interaction \n(HCI) researchers of different backgrounds. The case studies and test runs showed HRIGC \nto be a promising tool in articulating the key aspects of HRI game design in an intuitive \nmanner. Formal validation of the canvas is necessary to confirm this tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".