Artificial intelligence and perceived effort in relationship maintenance: Effects on relationship satisfaction and uncertainty
Bibliographic record
Abstract
Maintaining satisfying close relationships is important for individuals’ well-being. In the digital age, artificial intelligence (AI) has growing applications for relationship maintenance and thus implications for relational well-being. We hypothesize that although using AI to help with relational maintenance may reduce an individual’s effort, their partner may perceive AI-augmented activities negatively. According to the investment model and equity theory, perceptions of diminished effort in a relationship may lead to less satisfaction and greater uncertainty about the partner’s involvement in the relationship. In an online experiment, we presented participants ( N = 208) with hypothetical scenarios of relational maintenance initiated by a fictional close friend, with a 3 (agency: self-without-augmentation vs. AI-augmented vs. human-augmented) × 3 (relational task: support-giving vs. advice-giving vs. birthday celebration) between-subjects design. Compared to the self-without-augmentation condition (i.e., the control condition) where the friend completed a relational task with no external aid, using AI assistance led participants to perceive the friend expended less effort, reducing participants’ relationship satisfaction and increasing uncertainty. Getting help from another person was not significantly different from using AI in terms of perceived partner effort, relationship satisfaction, uncertainty, and perceived appropriateness. We discuss the implications of the findings for relational maintenance and technology-mediated communication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".