Skip to main content

Educating students on the behavioral and psychological aspects of romance scam victimization via a social engineering competition

Bleiman, Rachel ; Park, Hwanhee ; Rege, Aunshul (2025) — Journal of Cybersecurity Education, Research and Practice

Citation activity

Total citations · Google Scholar
Unavailable
Average per calendar year
Citation count unavailable

Counts reflect Google Scholar’s coverage and do not measure research quality. Source, calculation and limitations

What do these research terms mean?
Preprint
A manuscript shared before formal peer review and publication. Check whether a later published version is available.
Dataset
A collection of data or examples for others to inspect or reuse. It can appear in Library search and topic mapping, but RSRC does not use dataset records as evidence in Research Insights.
Dissertation or thesis
Research submitted for an academic degree. This describes its format, not its reliability.
Journal article
An article published in a journal. This label alone does not establish peer review, study quality, or how well the findings apply elsewhere.
Qualitative research
Examines experiences, meanings, or processes, often through interviews or observations. It can explain how something happens without estimating how common it is.
Quantitative research
Uses numerical measurements to describe patterns or test relationships. A relationship between two measurements does not by itself show that one causes the other.
Systematic review
Uses a planned, documented method to find and assess research addressing a question. Its conclusions still depend on the included studies and what the search covered.
Meta-analysis
Statistically combines results from multiple studies. Combining studies does not remove weaknesses in their design or make unlike populations interchangeable.
Not classified
This record has no recognized label in this filter. It does not mean the publication used no method, or that no research exists.

Definitions draw on DataCite resource types; Cochrane review methods; NLM: association and causation. RSRC’s dataset and classification rules are explained in our methodology.

Citation tools


              
              

Transparency

Evidence and review status

This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

Source basis
Downloaded PDF
Source updates
No notice found at last check
AI-generated page content
Yes
Automated checks
Passed
Administrative approval
Yes
Human content review
Not recorded
Subject-matter-expert review
Not recorded
How this was prepared
Source basis

RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.

  • PDF added to RSRC:
Source updates

No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.

  • Last source-status attempt:
AI-generated page content

AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.

  • Document analysis recorded:
  • Page record updated:
Automated checks

The current, source-bound synopsis passed the recorded versioned publication checks.

  • Checks completed:
View passed checks (3)
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
  • Numerical claims checked against the available source text
  • English-source lexical grounding check
Administrative approval

An authenticated administrator approved the bibliographic record for public Library display. This is not a review of every research claim.

  • Approved for public display:
Human content review

No human review is recorded for the AI-generated content displayed on this page.

Subject-matter-expert review

RSRC has not recorded review of this content by a subject-matter or methods expert.

Review-state definitions
Found a possible error? Request a correction.

Synopsis

The online dating sector generated 2.98 billion USD in 2023 and is projected to reach about 3.6 billion USD by 2025. Within this space, romance scams impose substantial financial losses, estimated at 1.3 billion USD in 2022, and they also inflict emotional and psychological harm on those affected. The article reports on findings from a 2023 Romance Scam and Social Engineering Competition (RSSEC), which introduced students to the behavioral and psychological dimensions of romance fraud and its broader implications for cybersecurity and victim safety. The competition was designed to illuminate how victims experience social engineering across the stages of a romance scam and to place participants in dual roles: as fraud fighters who interact with victims with respect and empathy, and as observers of scammers who rely on social engineering tricks. The paper details the design of the event, including elements that incorporated artificial intelligence, along with its overall structure and logistical setup. It also shares insights into what students learned about the use of psychological persuasion to manipulate victims during the scam process and about their capacity to collaborate effectively as defenders against fraud. Findings show that participants developed a clearer understanding of how psychological persuasion operates within romance scams and demonstrated the ability to work cohesively as a team of fraud fighters. The RSSEC afforded students the opportunity to treat scam victims with dignity by applying tactical empathy, a key capability for appreciating the behavioral and psychological aspects involved in cybercrime victimization. Overall, the study highlights how experiential competition formats can advance awareness of social engineering tactics and promote ethical, victim-centered responses among future professionals in the field.

Identified Gaps

The paper identifies a lack of experiential-learning resources focused specifically on social engineering that do not require technical knowledge or prerequisites. Existing cybersecurity experiential learning opportunities tend to emphasize technical cybersecurity while only partially addressing social engineering. The authors position the romance-scam competition as a hands-on educational resource intended to address this gap.

Methods

The study reports findings from a virtual, three-day 2023 Romance Scam and Social Engineering Competition involving 16 student teams across high-school, undergraduate, and graduate tracks. Teams investigated a simulated elderly romance-scam victim, interacted with simulated friends, victim, and scammer, analyzed persuasion tactics and evidence, and delivered an empathetic debrief and victim checklist. The scenario used AI-generated visual material. Participant demographics, pre/post confidence, and feedback on task difficulty, preparation, teamwork, and relevance were collected and summarized.

Limitations

The competition used one simulated romance-scam case and therefore captured only a cross-section of possible romance-scam manifestations. The authors state that the scenario is not generalizable to all romance scams, does not comprehensively represent scams across multiple platforms, and cannot capture every nuance or the full range of scam possibilities. Findings concern a focused student learning opportunity rather than actual victim experiences or a comprehensive account of romance fraud.

Future Work

Future competitions or research should examine different variations of romance scams and other scams, involve participants beyond students, and assess the competition’s long-term effects on students’ professional practices or attitudes. The authors also plan to use AI in future competition design and live engagements, including education about AI-enabled social engineering such as voice cloning in vishing.

See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.