Skip to main content
Log in

Automatic generation and recommendation of personalized challenges for gamification

  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript


Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster end users’ engagement and to induce a change in their behavior. Despite its impact potential, well-known limitations concern retaining players and sustaining over time the newly adopted behavior. This problem can be sourced from two common errors: basic game elements that are considered at design time and a one-size-fits-all strategy in generating game content. The former issue refers to the fact that most gamified applications focus only on the superficial layer of game design elements, such as points, badges and leaderboards, and do not exploit the full potential of games in terms of engagement and motivation; the latter relates to a lack of personalization, since the game content proposed to players does not take into consideration their specific abilities, skills and preferences. Taken together, these issues often lead to players’ boredom or frustration. The game element of challenges, which propose a demanding but achievable goal and rewarding completion, has empirically proved effective to keep players’ interest alive and to sustain their engagement over time. However, they require a significant effort from game designers, who must periodically conceive new challenges, align goals with the objectives of the gamification campaign, balance those goals with rewards and define assignment criteria to the player population. Our hypothesis is that we can overcome these limitations by automatically generating challenges, which are personalized to each individual player throughout the game. To this end, we have designed and implemented a fully automated system for the dynamic generation and recommendation of challenges, which are personalized and contextualized based on the preferences, history, game status and performances of each player. The proposed approach is generic and can be applied in different gamification application contexts. In this paper, we present its implementation within a large-scale and long-running open-field experiment promoting sustainable urban mobility that lasted 12 weeks and involved more than 400 active players. A comparative evaluation is performed, considering challenges that are generated and assigned fully automatically through our system versus analogous challenges developed and assigned by human game designers. The evaluation covers the acceptance of challenges by players, the impact induced on players’ behavior, as well as the efficiency in terms of rewarding cost. The evaluation results are very encouraging and suggest that procedural content generation applied to the customization of challenges has a great potential to enhance the performance of gamification applications and augment their engagement and persuasive power.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others


  1. See


  3. This may configure, at the very beginning of the game, a sort of cold start problem; to bypass that, one can have an initial phase of the game without injection of challenges (2 weeks, in our game), in which sufficient game data are collected; alternatively, one can leverage data from previous instantiations of the same game, or any other suitable statistics, to establish an initial baseline distribution.

  4. Non-RS challenges were managed by Trento Play&Go game administration team, who were involved in the design and day-to-day management also of two previous editions of the game, and had become very knowledgeable of the game mechanics and dynamics, as well as of the urban mobility gamification domain.

  5. We used TOST function which is available in “TOSTER” package in r:

  6. For those estimates, we took advantage of a function made available to the R statistical suite by Jurasinski and Günther (2014).

  7. In order to avoid confusion with AUC, which usually indicates the area under the curve in a receiver operating characteristic (ROC) plot.


  • Andersen, E., Gulwani, S., Popovic, Z.: A trace-based framework for analyzing and synthesizing educational progressions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13, pp. 773–782 (2013). ISBN 978-1-4503-1899-0

  • Aponte, M.-V., Levieux, G., Natkin, S.: Measuring the level of difficulty in single player video games. Entertain. Comput. 2(4), 205–213 (2011)

    Article  Google Scholar 

  • Bakkes, S., Whiteson, S., Li, G., Vişniuc, G.V., Charitos, E., Heijne, N., Swellengrebel, A.: Challenge balancing for personalised game spaces. In: 2014 IEEE Games Media Entertainment, pp. 1–8. IEEE (2014)

  • Bartle, R.: Hearts, clubs, diamonds, spades: players who suit muds. J. MUD Res. 1(1), 19 (1996)

    Google Scholar 

  • Bartle, R.: Designing Virtual Worlds. New Riders Games, Indianapolis (2003)

    Google Scholar 

  • Beau, P., Bakkes, S.: Automated game balancing of asymmetric video games. In: 2016 IEEE Conference on Computational Intelligence and Games (CIG), pp 1–8. IEEE (2016)

  • Blackwelder, W.C.: Proving the null hypothesis in clinical trials. Control. Clin. Trials 3(4), 345–353 (1982)

    Article  Google Scholar 

  • Blom, P.M., Bakkes, S., Tan, C.T., Whiteson, S., Roijers, D., Valenti, R., Gevers, T.: Towards personalised gaming via facial expression recognition. In: Tenth Artificial Intelligence and Interactive Digital Entertainment Conference (2014)

  • Brewer, R.S., Verdezoto, N., Holst, T., Rasmussen, M.K.: Tough shift: exploring the complexities of shifting residential electricity use through a casual mobile game. In: Proceedings of the 2015 Annual Symposium on Computer–Human Interaction in Play. ACM (2015)

  • Broll, G., Cao, H., Ebben, P., Holleis, P., Jacobs, K., Koolwaaij, J., Luther, M., Souville, B.: Tripzoom: An app to improve your mobility behavior. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, pp. 57:1–57:4. ACM (2012). ISBN 978-1-4503-1815-0

  • Butler, E., Andersen, E., Smith, A.M., Gulwani, S., Popović, Z.: Automatic game progression design through analysis of solution features. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15, pp. 2407–2416. ACM (2015)

  • Charles, D., Kerr, A., McNeill, M., McAlister, M., Black, M., Kcklich, J., Moore, A., Stringer, K.: Player-centred game design: player modelling and adaptive digital games. In: Proceedings of the Digital Games Research Conference, vol. 285 (2005)

  • Chen, J.: Flow in games (and everything else). Commun. ACM 50(4), 31–34 (2007). ISSN 0001-0782

    Article  Google Scholar 

  • Cheng, R., Vassileva, J.: Design and evaluation of an adaptive incentive mechanism for sustained educational online communities. User Model. User Adapt. Interact. 16(3–4), 321–348 (2006)

    Article  Google Scholar 

  • Chou, Y.-K.: Actionable Gamification: Beyond Points, Badges, and Leaderboards. Octalysis Media, Milpitas (2015)

    Google Scholar 

  • Coenen, T., Merchant, P., Laureyssens, T., Claeys, L., Criel, J.: Zwerm: stimulating urban neighborhood self-organization through gamification. In: Using ICT, Social Media and Mobile Technologies to Foster Self-Organisation in Urban and Neighbourhood Governance (2013)

  • Cooper, S., Deterding, C.S., Tsapakos, T.: Player rating systems for balancing human computation games. In: Proceedings of 1st International Joint Conference of DiGRA and FDG (2016)

  • Cowley, B., Charles, D., Black, M., Hickey, R.: Toward an understanding of flow in video games. Comput. Entertain. 6, 1–27 (2008)

    Article  Google Scholar 

  • Cowley, B., Moutinho, J.L., Bateman, C., Oliveira, A.: Learning principles and interaction design for ‘green my place’: a massively multiplayer serious game. Entertain. Comput. 2, 103–113 (2011)

    Article  Google Scholar 

  • Das, S., Zook, A., Riedl, M.O.: Examining game world topology personalization. In: Proceedings of the 33rd ACM Conference on Human Factors in Computing Systems, CHI ’15, pp. 3731–3734 (2015)

  • Di Salvo, C., Sengers, P., Brynjarsdóttir, H.: Mapping the landscape of sustainable HCI. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1975–1984. ACM (2010)

  • Du, J., Feng, Y., Zhou, C.: Gamification for behavior change of occupants in campus buildings to affect improved energy efficiency (2014)

  • Elo, A.E.: The Rating of Chessplayers, Past and Present. Arco Pub., Nagoya (1978)

    Google Scholar 

  • Farzan, R., DiMicco, J.M., Millen, D.R., Dugan, C., Geyer, W., Brownholtz, E.A.: Results from deploying a participation incentive mechanism within the enterprise. pp. 563–572. ACM (2008)

  • Ferron, M., Loria, E., Marconi, A., Massa, P.: Play&go, an urban game promoting behaviour change for sustainable mobility. Interact. Des. Architect. J. 40, 24–45 (2019)

    Google Scholar 

  • Fogg, B.J.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann Publishers Inc., Burlington (2002)

    Google Scholar 

  • Froehlich, J., Dillahunt, T., Klasnja, P.V., Mankoff, J., Consolvo, S., Harrison, B.L., Landay, J.A.: Ubigreen: investigating a mobile tool for tracking and supporting green transportation habits. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, April 4–9, 2009, pp. 1043–1052 (2009)

  • Gabrielli, S., Maimone, R., Forbes, P., Masthoff, J., Wells, S., Primerano, L., Haverinen, L., Bo, G., Pompa, M.: Designing motivational features for sustainable urban mobility. In: CHI’13 Extended Abstracts on Human Factors in Computing Systems, pp. 1461–1466. ACM (2013)

  • Gehan, E.A.: A generalized wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika 52(1–2), 203–224 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  • Gordillo, A., Gallego, D., Barra, E., Quemada, J.: The city as a learning gamified platform. In: Frontiers in Education Conference, pp. 372–378. IEEE (2013)

  • Greengard, S.: Tracking garbage. Commun. ACM 53(3), 19–20 (2010). ISSN 0001-0782

    Article  Google Scholar 

  • Hamari, J., Koivisto, J., Pakkanen, T.: Do persuasive technologies persuade?—a review of empirical studies. In: International Conference on Persuasive Technology, pp. 118–136. Springer (2014a)

  • Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work?—a literature review of empirical studies on gamification. In: 47th Hawaii International Conference on System Sciences. IEEE (2014b)

  • Harrison, B., Ware, S.G., Fendt, M.W., Roberts, D.L.: A survey and analysis of techniques for player behavior prediction in massively multiplayer online role-playing games. IEEE Trans. Emerg. Top. Comput. 3(2), 260–274 (2015)

    Article  Google Scholar 

  • Hendrikx, M., Meijer, S., Van Der Velden, J., Iosup, A.: Procedural content generation for games: a survey. ACM Trans. Multimedia Comput. Commun. Appl. 9(1), 1–22 (2013). ISSN 1551-6857

    Article  Google Scholar 

  • Hojer, M., Wangel, J.: Smart sustainable cities: definition and challenges. Volume 310 of Advances in Intelligent Systems and Computing, pp. 333–349. Springer International Publishing (2015)

  • Hsieh, J.-L., Sun, C.-T.: Building a Player Strategy Model by Analyzing Replays of Real-time Strategy Games. pp. 3106–3111. IEEE (2008)

  • Huang, E.M.: Building outwards from sustainable HCI. ACM Interact. 18(3), 14–17 (2011)

    Article  Google Scholar 

  • Hunicke, R.: The case for dynamic difficulty adjustment in games. In: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pp. 429–433. ACM (2005)

  • Jurasinski, G., Günther, A.: Flux: Flux Rate Calculation from Dynamic Closed Chamber Measurements. R package version 0.1-4, Rostock (2014)

  • Karpinskyj, S., Zambetta, F., Cavedon, L.: Video game personalisation techniques: a comprehensive survey. Entertain. Comput. 5(4), 211–218 (2014)

    Article  Google Scholar 

  • Kazhamiakin, R., Marconi, A., Perillo, M., Pistore, M., Valetto, G., Piras, L., Avesani, F., Perri, N.: Using gamification to incentivize sustainable urban mobility. In: 2015 IEEE First International Smart Cities Conference (ISC2), pp. 1–6 (2015)

  • Kazhamiakin, R., Marconi, A., Martinelli, A., Pistore, M., Valetto, G.: A gamification framework for the long-term engagement of smart citizens. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–7. IEEE (2016)

  • Khajah, M.M., Roads, Brett D., Lindsey, R.V., Liu, Y.-E., Mozer, M.C.: Designing engaging games using bayesian optimization. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 5571–5582 (2016). ISBN 978-1-4503-3362-7

  • Khoshkangini, R., Valetto, G., Marconi, A.: Generating personalized challenges to enhance the persuasive power of gamification. In: International Workshop on Personalized Persuasive Technologies (2017)

  • Khoshkangini, R, Ontañón, S., Marconi, A., Zhu, J.: Dynamically extracting play style in educational games. In: EUROSIS Proceedings, GameOn (2018)

  • Lampe, C.A.: Ratings use in an online discussion system: The slashdot case (2006)

  • Lee, Joey J., Matamoros, E., Kern, R., Marks, J., de Luna, C., Jordan-Cooley, W.: Greenify: fostering sustainable communities via gamification. In: CHI’13 Extended Abstracts on Human Factors in Computing Systems, pp. 1497–1502. ACM (2013)

  • Lessel, P., Altmeyer, M., Krüger, A.: Analysis of recycling capabilities of individuals and crowds to encourage and educate people to separate their garbage playfully. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015 (2015)

  • Liu, C., Agrawal, P., Sarkar, N., Chen, S.: Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. Int. J. Hum. Comput. Interact. 25(6), 506–529 (2009)

    Article  Google Scholar 

  • Lopes, R., Bidarra, R.: Adaptivity challenges in games and simulations: a survey. IEEE Trans. Comput.Iintell. AI Games 3(2), 85–99 (2011)

    Article  Google Scholar 

  • Lora, D., Sánchez-Ruiz, A.A., González-Calero, P.A., Gómez-Martín, M.A.: Dynamic difficulty adjustment in tetris. In: FLAIRS Conference, pp. 335–339 (2016)

  • Loria, E., Marconi, A.: Player Types and player behaviors: analyzing correlations in an on-the-field gamified system. In: Proceedings of the 2018 Annual Symposium on Computer–Human Interaction in Play Companion Extended Abstracts—CHI PLAY ’18 Extended Abstracts, pp. 531–538. ACM Press, Melbourne, VIC, Australia (2018)

  • Machado, M.C., Fantini, E.P.C., Chaimowicz, L.: Player modeling: towards a common taxonomy. In: 2011 16th International Conference on Computer Games (CGAMES), pp. 50–57. IEEE (2011)

  • Marconi, A., Schiavo, G., Zancanaro, M., Valetto, G., Pistore, M.: Exploring the world through small green steps: improving sustainable school transportation with a game-based learning interface. In: Proceedings of the 2018 International Conference on Advanced Visual Interfaces, AVI ’18, pp. 24:1–24:9. ACM, New York, NY, USA (2018)

  • Monterrat, B., Desmarais, M., Lavoué, E., George, S.: A player model for adaptive gamification in learning environments. In: International Conference on Artificial Intelligence in Education, pp. 297–306. Springer (2015)

  • Orland, B., Ram, N., Lang, D., Houser, K., Kling, N., Coccia, M.: Saving energy in an office environment: a serious game intervention. Energy Build. 74, 43–52 (2014). ISSN 0378-7788

    Article  Google Scholar 

  • Pizzi, D., Lugrin, J.-L., Whittaker, A., Cavazza, M.: Automatic generation of game level solutions as storyboards. IEEE Trans. Comput. Intell. AI Games 2, 149–161 (2010)

    Article  Google Scholar 

  • Raffe, W.L., Zambetta, F., Li, X.: Neuroevolution of content layout in the PCG: angry bots video game. In: 2013 IEEE Congress on Evolutionary Computation, pp. 673–680 (2013)

  • Robinson, A.P., Froese, R.E.: Model validation using equivalence tests. Ecol. Model. 176(3–4), 349–358 (2004)

    Article  Google Scholar 

  • Schuirmann, D.J.: A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J. Pharmacokinet. Biopharm. 15(6), 657–680 (1987)

    Article  Google Scholar 

  • Shiraishi, M., Washio, Y., Takayama, C., Lehdonvirta, V., Kimura, H., Nakajima, T.: Using individual, social and economic persuasion techniques to reduce Co\(_{2}\) emissions in a family setting. In: Proceedings of the 4th International Conference on Persuasive Technology, pp. 1–13. ACM (2009)

  • Sifa, R., Bauckhage, C., Drachen, A.: Archetypal game recommender systems. In: LWA, pp. 45–56. Citeseer (2014)

  • Silva, F., Analide, C., Rosa, L., Felgueiras, G., Pimenta, C.: Gamification, social networks and sustainable environments. Int. J. Interact. Multimedia Artif. Intell. 2, 52–59 (2013)

    Google Scholar 

  • Simon, J., Jahn, M., Al-Akkad, A.: Saving energy at work: the design of a pervasive game for office spaces. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia. ACM (2016)

  • Skocir, P., Marusic, L., Marusic, M., Petric, A.: The MARS–A Multi-Agent Recommendation System for Games on Mobile Phones, pp. 104–113. Springer, Berlin Heidelberg (2012)

    Google Scholar 

  • Soares de Lima, E., Feijó, B., Furtado, A.L.: Hierarchical generation of dynamic and nondeterministic quests in games. In: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology, pp. 24–1. ACM (2014)

  • Togelius, J., De Nardi, R., Lucas, S.M.: Towards automatic personalised content creation for racing games. In: 2007 IEEE Symposium on Computational Intelligence and Games, pp. 252–259 (2007)

  • Togelius, J., Yannakakis, G.N., Stanley, K.O., Browne, C.: Search-based procedural content generation: a taxonomy and survey. IEEE Trans. Comput. Intell. AI Games 3(3), 172–186 (2011)

    Article  Google Scholar 

  • Tulloch, R.: Reconceptualising gamification: play and pedagody. Digit. Cult. Educ. 6(4), 317–333 (2014)

    Google Scholar 

  • Valls-Vargas, J., Ontanón, S., Zhu, J.: Exploring player trace segmentation for dynamic play style prediction. In: Proceedings of the Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 93–99 (2015)

  • Van Lankveld, G., Spronck, P., Van Den Herik, H.J., Rauterberg, M.: Incongruity-based adaptive game balancing. In: Advances in Computer Games, pp. 208–220. Springer (2009)

  • Weber, B.G., Mateas, M, Jhala, A.: Using data mining to model player experience. In: FDG Workshop on Evaluating Player Experience in Games (2011)

  • Weiser, P., Bucher, D., Cellina, F., De Luca, V.: A taxonomy of motivational affordances for meaningful gamified and persuasive technologies. In: Proceedings of the 3rd International Conference on ICT for Sustainability (ICT4S), Volume 22 of Advances in Computer Science Research, pp. 271–280. Atlantis Press (2015)

  • Yannakakis, G.N., Hallam, J.: Towards optimizing entertainment in computer games. Appl. Artif. Intell. 21(10), 933–971 (2007)

    Article  Google Scholar 

  • Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 2(3), 147–161 (2011)

    Article  Google Scholar 

  • Yannakakis, G.N., Togelius, J.: A panorama of artificial and computational intelligence in games. IEEE Trans. Comput. Intell. AI Games 7(4), 317–335 (2015)

    Article  Google Scholar 

  • Yannakakis, G.N., Martínez, H.P., Jhala, A.: Towards affective camera control in games. User Model. User Adapt. Interact. 20(4), 313–340 (2010)

    Article  Google Scholar 

  • Zlatow, M., Kelliher, A.: Increasing recycling behaviors through user-centered design. In: Proceedings of the 2007 Conference on Designing for User eXperiences, pp. 1–27. ACM (2007). ISBN 978-1-60558-308-2

  • Zook, A., Lee-Urban, S., Drinkwater, M.R., Riedl, M.O..: Skill-based mission generation: A data-driven temporal player modeling approach. In: Proceedings of the the Third Workshop on Procedural Content Generation in Games, pp. 1–8 (2012a)

  • Zook, A., Lee-Urban, S., Riedl, M.O., Holden, H.K., Sottilare, R.A., Brawner, K.W.: Automated scenario generation: toward tailored and optimized military training in virtual environments. In: Proceedings of the International Conference on the Foundations of Digital Games, pp. 164–171. ACM (2012b)

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Reza Khoshkangini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khoshkangini, R., Valetto, G., Marconi, A. et al. Automatic generation and recommendation of personalized challenges for gamification. User Model User-Adap Inter 31, 1–34 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: