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

View Original Source (opens in a new tab)

DOI: 10.62915/2472-2707.1204 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   AI-Enabled Fraud, AI Generated Personas, Experiential Learning, Fraud Fighter Training, Offender Tactics, Platform Policy, Psychological Persuasion, Psychology, Romance Scams, Social Engineering, Student Competition, Tactical Empathy, Victim Experience
Citation tools
Download RIS Download BibTeX 


              
              

            

          

        


      

      

    

        
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
                

            

                    

                
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:
                                    
                                
    • 
                                                    

                    
                                        

                

                            

                    
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.

          

              

    
    

        
Related Research Insights

        

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